workflows¶
Module: workflows.align¶
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The registration workflow is organized as a collection of different functions. |
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Check the dimensions of the input images. |
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Reslice data with new voxel resolution defined by |
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Save Quality Assurance metrics. |
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Utility function for registering large tractograms. |
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Transformation to align the center of mass of the input images. |
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Apply affine transformation to streamlines |
Module: workflows.base¶
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Module: workflows.combined_workflow¶
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Module: workflows.denoise¶
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Standard deviation estimation from local patches |
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Non-local means for denoising 3D and 4D images |
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Module: workflows.docstring_parser¶
This was taken directly from the file docscrape.py of numpydoc package.
Copyright (C) 2008 Stefan van der Walt <stefan@mentat.za.net>, Pauli Virtanen <pav@iki.fi>
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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A line-based string reader. |
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Deindent a list of lines maximally |
Issue a warning, or maybe ignore it or raise an exception. |
Module: workflows.flow_runner¶
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Transforms the logging level passed on the commandline into a proper logging level name. |
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Wraps the process of building an argparser that reflects the workflow that we want to run along with some generic parameters like logging, force and output strategies. |
Module: workflows.io¶
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Get the names and default values of a callable object’s parameters. |
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Return all members of an object as (name, value) pairs sorted by name. |
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Return true if the object is a user-defined function. |
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Module: workflows.mask¶
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Module: workflows.multi_io¶
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Create output filenames that work nicely with multiple input files from multiple directories (processing multiple subjects with one command) |
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Return the longest common substring from the beginning of sa and sb. |
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Concatenate list of inputs |
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Generates a list of output files paths based on input files and output strategies. |
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Return a list of paths matching a pathname pattern. |
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Creates an IOIterator from the parameters. |
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Creates an IOIterator using introspection. |
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Module: workflows.reconst¶
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Implementation of Constant Solid Angle reconstruction method. |
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Class for the Diffusion Kurtosis Model |
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Diffusion Tensor |
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Selector function to switch between the 2-stage Trust-Region Reflective based NLLS fitting method (also containing the linear fit): trr and the Variable Projections based fitting method: varpro. |
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Automatic estimation of response function using FA. |
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Axial Diffusivity (AD) of a diffusion tensor. |
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Color fractional anisotropy of diffusion tensor |
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Fractional anisotropy (FA) of a diffusion tensor. |
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Geodesic anisotropy (GA) of a diffusion tensor. |
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Mode (MO) of a diffusion tensor [1]. |
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A general function for creating diffusion MR gradients. |
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Safely evaluate an expression node or a string containing a Python expression. |
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Returns the six lower triangular values of the tensor and a dummy variable if b0 is not None |
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Mean Diffusivity (MD) of a diffusion tensor. |
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Returns a Nifti1Image with a symmetric matrix intent |
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Fit the model to data and computes peaks and metrics |
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Save SH, directions, indices and values of peaks to Nifti. |
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Radial Diffusivity (RD) of a diffusion tensor. |
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Read b-values and b-vectors from disk |
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Save all important attributes of object PeaksAndMetrics in a PAM5 file (HDF5). |
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Extract the diffusion tensor eigenvalues, the diffusion tensor eigenvector matrix, and the 15 independent elements of the kurtosis tensor from the model parameters estimated from the DKI model |
Issue a warning, or maybe ignore it or raise an exception. |
Module: workflows.segment¶
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Simple brain extraction tool method for images from DWI data. |
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Return the current time in seconds since the Epoch. |
Module: workflows.stats¶
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Diffusion Tensor |
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Multi-dimensional binary dilation with the given structuring element. |
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Compute the bounding box of nonzero intensity voxels in the volume. |
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Applies statistical analysis on bundles and saves the results in a directory specified by |
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A general function for creating diffusion MR gradients. |
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Simple brain extraction tool method for images from DWI data. |
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Return package-like thing and module setup for package name |
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Read b-values and b-vectors from disk |
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Segment the cfa inside roi using the values from threshold as bounds. |
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Saves the simple plot with given x and y values |
Module: workflows.tracking¶
cdef: |
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A direction getter that returns the closest odf peak to previous tracking direction. |
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Continuous map criterion (CMC) stopping criterion from [1]. |
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Return direction of a sphere with the highest probability mass function (pmf). |
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Randomly samples direction of a sphere based on probability mass function (pmf). |
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Enum to simplify future change to convention |
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Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). |
# Declarations from stopping_criterion.pxd bellow cdef: double threshold, interp_out_double[1] double[:] interp_out_view = interp_out_view double[:, :, :] metric_map |
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Load a PeaksAndMetrics HDF5 file (PAM5) |
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Save the stateful tractogram in any format (trk, tck, vtk, fib, dpy) |
Module: workflows.viz¶
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Highly interactive visualization - invert the Horizon! |
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Load a PeaksAndMetrics HDF5 file (PAM5) |
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Join two or more pathname components, inserting ‘/’ as needed. |
Module: workflows.workflow¶
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Creates an IOIterator using introspection. |
AffineMap¶
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class
dipy.workflows.align.AffineMap(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)¶ Bases:
objectMethods
Return the value of the transformation, not a reference.
set_affine(affine)Set the affine transform (operating in physical space).
transform(image[, interp, image_grid2world, …])Transform the input image from co-domain to domain space.
transform_inverse(image[, interp, …])Transform the input image from domain to co-domain space.
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__init__(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)¶ AffineMap
Implements an affine transformation whose domain is given by domain_grid and domain_grid2world, and whose co-domain is given by codomain_grid and codomain_grid2world.
The actual transform is represented by the affine matrix, which operate in world coordinates. Therefore, to transform a moving image towards a static image, we first map each voxel (i,j,k) of the static image to world coordinates (x,y,z) by applying domain_grid2world. Then we apply the affine transform to (x,y,z) obtaining (x’, y’, z’) in moving image’s world coordinates. Finally, (x’, y’, z’) is mapped to voxel coordinates (i’, j’, k’) in the moving image by multiplying (x’, y’, z’) by the inverse of codomain_grid2world. The codomain_grid_shape is used analogously to transform the static image towards the moving image when calling transform_inverse.
If the domain/co-domain information is not provided (None) then the sampling information needs to be specified each time the transform or transform_inverse is called to transform images. Note that such sampling information is not necessary to transform points defined in physical space, such as stream lines.
- Parameters
- affinearray, shape (dim + 1, dim + 1)
the matrix defining the affine transform, where dim is the dimension of the space this map operates in (2 for 2D images, 3 for 3D images). If None, then self represents the identity transformation.
- domain_grid_shapesequence, shape (dim,), optional
the shape of the default domain sampling grid. When transform is called to transform an image, the resulting image will have this shape, unless a different sampling information is provided. If None, then the sampling grid shape must be specified each time the transform method is called.
- domain_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the domain grid. If None (the default), then the grid-to-world transform is assumed to be the identity.
- codomain_grid_shapesequence of integers, shape (dim,)
the shape of the default co-domain sampling grid. When transform_inverse is called to transform an image, the resulting image will have this shape, unless a different sampling information is provided. If None (the default), then the sampling grid shape must be specified each time the transform_inverse method is called.
- codomain_grid2worldarray, shape (dim + 1, dim + 1)
the grid-to-world transform associated with the co-domain grid. If None (the default), then the grid-to-world transform is assumed to be the identity.
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get_affine()¶ Return the value of the transformation, not a reference.
- Returns
- affinendarray
Copy of the transform, not a reference.
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set_affine(affine)¶ Set the affine transform (operating in physical space).
Also sets self.affine_inv - the inverse of affine, or None if there is no inverse.
- Parameters
- affinearray, shape (dim + 1, dim + 1)
the matrix representing the affine transform operating in physical space. The domain and co-domain information remains unchanged. If None, then self represents the identity transformation.
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transform(image, interp='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)¶ Transform the input image from co-domain to domain space.
By default, the transformed image is sampled at a grid defined by self.domain_shape and self.domain_grid2world. If such information was not provided then sampling_grid_shape is mandatory.
- Parameters
- image2D or 3D array
the image to be transformed
- interpstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with image. If None (the default), then the grid-to-world transform is assumed to be the identity.
- sampling_grid_shapesequence, shape (dim,), optional
the shape of the grid where the transformed image must be sampled. If None (the default), then self.codomain_shape is used instead (which must have been set at initialization, otherwise an exception will be raised).
- sampling_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the sampling grid (specified by sampling_grid_shape, or by default self.codomain_shape). If None (the default), then the grid-to-world transform is assumed to be the identity.
- resample_onlyBoolean, optional
If False (the default) the affine transform is applied normally. If True, then the affine transform is not applied, and the input image is just re-sampled on the domain grid of this transform.
- Returns
- transformedarray, shape sampling_grid_shape or
self.codomain_shape
the transformed image, sampled at the requested grid
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transform_inverse(image, interp='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)¶ Transform the input image from domain to co-domain space.
By default, the transformed image is sampled at a grid defined by self.codomain_shape and self.codomain_grid2world. If such information was not provided then sampling_grid_shape is mandatory.
- Parameters
- image2D or 3D array
the image to be transformed
- interpstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with image. If None (the default), then the grid-to-world transform is assumed to be the identity.
- sampling_grid_shapesequence, shape (dim,), optional
the shape of the grid where the transformed image must be sampled. If None (the default), then self.codomain_shape is used instead (which must have been set at initialization, otherwise an exception will be raised).
- sampling_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the sampling grid (specified by sampling_grid_shape, or by default self.codomain_shape). If None (the default), then the grid-to-world transform is assumed to be the identity.
- resample_onlyBoolean, optional
If False (the default) the affine transform is applied normally. If True, then the affine transform is not applied, and the input image is just re-sampled on the domain grid of this transform.
- Returns
- transformedarray, shape sampling_grid_shape or
self.codomain_shape
the transformed image, sampled at the requested grid
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AffineRegistration¶
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class
dipy.workflows.align.AffineRegistration(metric=None, level_iters=None, sigmas=None, factors=None, method='L-BFGS-B', ss_sigma_factor=None, options=None, verbosity=1)¶ Bases:
objectMethods
optimize(static, moving, transform, params0)Start the optimization process.
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__init__(metric=None, level_iters=None, sigmas=None, factors=None, method='L-BFGS-B', ss_sigma_factor=None, options=None, verbosity=1)¶ Initialize an instance of the AffineRegistration class.
- Parameters
- metricNone or object, optional
an instance of a metric. The default is None, implying the Mutual Information metric with default settings.
- level_iterssequence, optional
the number of iterations at each scale of the scale space. level_iters[0] corresponds to the coarsest scale, level_iters[-1] the finest, where n is the length of the sequence. By default, a 3-level scale space with iterations sequence equal to [10000, 1000, 100] will be used.
- sigmassequence of floats, optional
custom smoothing parameter to build the scale space (one parameter for each scale). By default, the sequence of sigmas will be [3, 1, 0].
- factorssequence of floats, optional
custom scale factors to build the scale space (one factor for each scale). By default, the sequence of factors will be [4, 2, 1].
- methodstring, optional
optimization method to be used. If Scipy version < 0.12, then only L-BFGS-B is available. Otherwise, method can be any gradient-based method available in dipy.core.Optimize: CG, BFGS, Newton-CG, dogleg or trust-ncg. The default is ‘L-BFGS-B’.
- ss_sigma_factorfloat, optional
If None, this parameter is not used and an isotropic scale space with the given factors and sigmas will be built. If not None, an anisotropic scale space will be used by automatically selecting the smoothing sigmas along each axis according to the voxel dimensions of the given image. The ss_sigma_factor is used to scale the automatically computed sigmas. For example, in the isotropic case, the sigma of the kernel will be \(factor * (2 ^ i)\) where \(i = 1, 2, ..., n_scales - 1\) is the scale (the finest resolution image \(i=0\) is never smoothed). The default is None.
- optionsdict, optional
extra optimization options. The default is None, implying no extra options are passed to the optimizer.
- verbosity: int (one of {0, 1, 2, 3}), optional
Set the verbosity level of the algorithm: 0 : do not print anything 1 : print information about the current status of the algorithm 2 : print high level information of the components involved in
the registration that can be used to detect a failing component.
- 3print as much information as possible to isolate the cause
of a bug.
Default: 1
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docstring_addendum= 'verbosity: int (one of {0, 1, 2, 3}), optional\n Set the verbosity level of the algorithm:\n 0 : do not print anything\n 1 : print information about the current status of the algorithm\n 2 : print high level information of the components involved in\n the registration that can be used to detect a failing\n component.\n 3 : print as much information as possible to isolate the cause\n of a bug.\n Default: 1\n '¶
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optimize(static, moving, transform, params0, static_grid2world=None, moving_grid2world=None, starting_affine=None, ret_metric=False)¶ Start the optimization process.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- transforminstance of Transform
the transformation with respect to whose parameters the gradient must be computed
- params0array, shape (n,)
parameters from which to start the optimization. If None, the optimization will start at the identity transform. n is the number of parameters of the specified transformation.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
- starting_affinestring, or matrix, or None, optional
- If string:
‘mass’: align centers of gravity ‘voxel-origin’: align physical coordinates of voxel (0,0,0) ‘centers’: align physical coordinates of central voxels
- If matrix:
array, shape (dim+1, dim+1).
- If None:
Start from identity.
The default is None.
- ret_metricboolean, optional
if True, it returns the parameters for measuring the similarity between the images (default ‘False’). The metric containing optimal parameters and the distance between the images.
- Returns
- affine_mapinstance of AffineMap
the affine resulting affine transformation
- xoptoptimal parameters
the optimal parameters (translation, rotation shear etc.)
- foptSimilarity metric
the value of the function at the optimal parameters.
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AffineTransform3D¶
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class
dipy.workflows.align.AffineTransform3D¶ Bases:
dipy.align.transforms.TransformMethods
get_identity_parametersParameter values corresponding to the identity transform
jacobianJacobian function of this transform
param_to_matrixMatrix representation of this transform with the given parameters
get_dim
get_number_of_parameters
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__init__()¶ Affine transform in 3D
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ApplyTransformFlow¶
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class
dipy.workflows.align.ApplyTransformFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
get_short_name()Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(static_image_files, moving_image_files, …)- Parameters
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__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
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run(static_image_files, moving_image_files, transform_map_file, transform_type='affine', out_dir='', out_file='transformed.nii.gz')¶ - Parameters
- static_image_filesstring
Path of the static image file.
- moving_image_filesstring
Path of the moving image(s). It can be a single image or a folder containing multiple images.
- transform_map_filestring
For the affine case, it should be a text(*.txt) file containing the affine matrix. For the diffeomorphic case, it should be a nifti file containing the mapping displacement field in each voxel with this shape (x, y, z, 3, 2)
- transform_typestring, optional
Select the transformation type to apply between ‘affine’ or ‘diffeomorphic’. (default affine)
- out_dirstring, optional
Directory to save the transformed files (default ‘’).
- out_filestring, optional
- Name of the transformed file (default ‘transformed.nii.gz’).
- It is recommended to use the flag –mix-names to
prevent the output files from being overwritten.
CCMetric¶
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class
dipy.workflows.align.CCMetric(dim, sigma_diff=2.0, radius=4)¶ Bases:
dipy.align.metrics.SimilarityMetricMethods
Computes one step bringing the static image towards the moving.
Computes one step bringing the moving image towards the static.
Frees the resources allocated during initialization
Numerical value assigned by this metric to the current image pair
Prepares the metric to compute one displacement field iteration.
set_levels_above(levels)Informs the metric how many pyramid levels are above the current one
set_levels_below(levels)Informs the metric how many pyramid levels are below the current one
set_moving_image(moving_image, …)Sets the moving image being compared against the static one.
set_static_image(static_image, …)Sets the static image being compared against the moving one.
use_moving_image_dynamics(…)This is called by the optimizer just after setting the moving image
use_static_image_dynamics(…)This is called by the optimizer just after setting the static image.
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__init__(dim, sigma_diff=2.0, radius=4)¶ Normalized Cross-Correlation Similarity metric.
- Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- sigma_diffthe standard deviation of the Gaussian smoothing kernel to
be applied to the update field at each iteration
- radiusint
the radius of the squared (cubic) neighborhood at each voxel to be considered to compute the cross correlation
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compute_backward()¶ Computes one step bringing the static image towards the moving.
Computes the update displacement field to be used for registration of the static image towards the moving image
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compute_forward()¶ Computes one step bringing the moving image towards the static.
Computes the update displacement field to be used for registration of the moving image towards the static image
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free_iteration()¶ Frees the resources allocated during initialization
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get_energy()¶ Numerical value assigned by this metric to the current image pair
Returns the Cross Correlation (data term) energy computed at the largest iteration
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initialize_iteration()¶ Prepares the metric to compute one displacement field iteration.
Pre-computes the cross-correlation factors for efficient computation of the gradient of the Cross Correlation w.r.t. the displacement field. It also pre-computes the image gradients in the physical space by re-orienting the gradients in the voxel space using the corresponding affine transformations.
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DiffeomorphicMap¶
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class
dipy.workflows.align.DiffeomorphicMap(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)¶ Bases:
objectMethods
allocate()Creates a zero displacement field
Inversion error of the displacement fields
expand_fields(expand_factors, new_shape)Expands the displacement fields from current shape to new_shape
Deformation field to transform an image in the backward direction
Deformation field to transform an image in the forward direction
Constructs a simplified version of this Diffeomorhic Map
interpret_matrix(obj)Try to interpret obj as a matrix
inverse()Inverse of this DiffeomorphicMap instance
Shallow copy of this DiffeomorphicMap instance
transform(image[, interpolation, …])Warps an image in the forward direction
transform_inverse(image[, interpolation, …])Warps an image in the backward direction
warp_endomorphism(phi)Composition of this DiffeomorphicMap with a given endomorphism
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__init__(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)¶ DiffeomorphicMap
Implements a diffeomorphic transformation on the physical space. The deformation fields encoding the direct and inverse transformations share the same domain discretization (both the discretization grid shape and voxel-to-space matrix). The input coordinates (physical coordinates) are first aligned using prealign, and then displaced using the corresponding vector field interpolated at the aligned coordinates.
- Parameters
- dimint, 2 or 3
the transformation’s dimension
- disp_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the deformation field’s discretization
- disp_grid2worldthe voxel-to-space transform between the def. fields
grid and space
- domain_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the default discretizatio of this map’s domain
- domain_grid2worldarray, shape (dim+1, dim+1)
the default voxel-to-space transformation between this map’s discretization and physical space
- codomain_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the images that are ‘normally’ warped using this transformation in the forward direction (this will provide default transformation parameters to warp images under this transformation). By default, we assume that the inverse transformation is ‘normally’ used to warp images with the same discretization and voxel-to-space transformation as the deformation field grid.
- codomain_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation of images that are ‘normally’ warped using this transformation (in the forward direction).
- prealignarray, shape (dim+1, dim+1)
the linear transformation to be applied to align input images to the reference space before warping under the deformation field.
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allocate()¶ Creates a zero displacement field
Creates a zero displacement field (the identity transformation).
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compute_inversion_error()¶ Inversion error of the displacement fields
Estimates the inversion error of the displacement fields by computing statistics of the residual vectors obtained after composing the forward and backward displacement fields.
- Returns
- residualarray, shape (R, C) or (S, R, C)
the displacement field resulting from composing the forward and backward displacement fields of this transformation (the residual should be zero for a perfect diffeomorphism)
- statsarray, shape (3,)
statistics from the norms of the vectors of the residual displacement field: maximum, mean and standard deviation
Notes
Since the forward and backward displacement fields have the same discretization, the final composition is given by
comp[i] = forward[ i + Dinv * backward[i]]
where Dinv is the space-to-grid transformation of the displacement fields
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expand_fields(expand_factors, new_shape)¶ Expands the displacement fields from current shape to new_shape
Up-samples the discretization of the displacement fields to be of new_shape shape.
- Parameters
- expand_factorsarray, shape (dim,)
the factors scaling current spacings (voxel sizes) to spacings in the expanded discretization.
- new_shapearray, shape (dim,)
the shape of the arrays holding the up-sampled discretization
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get_backward_field()¶ Deformation field to transform an image in the backward direction
Returns the deformation field that must be used to warp an image under this transformation in the backward direction (note the ‘is_inverse’ flag).
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get_forward_field()¶ Deformation field to transform an image in the forward direction
Returns the deformation field that must be used to warp an image under this transformation in the forward direction (note the ‘is_inverse’ flag).
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get_simplified_transform()¶ Constructs a simplified version of this Diffeomorhic Map
The simplified version incorporates the pre-align transform, as well as the domain and codomain affine transforms into the displacement field. The resulting transformation may be regarded as operating on the image spaces given by the domain and codomain discretization. As a result, self.prealign, self.disp_grid2world, self.domain_grid2world and self.codomain affine will be None (denoting Identity) in the resulting diffeomorphic map.
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interpret_matrix(obj)¶ Try to interpret obj as a matrix
Some operations are performed faster if we know in advance if a matrix is the identity (so we can skip the actual matrix-vector multiplication). This function returns None if the given object is None or the ‘identity’ string. It returns the same object if it is a numpy array. It raises an exception otherwise.
- Parameters
- objobject
any object
- Returns
- objobject
the same object given as argument if obj is None or a numpy array. None if obj is the ‘identity’ string.
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inverse()¶ Inverse of this DiffeomorphicMap instance
Returns a diffeomorphic map object representing the inverse of this transformation. The internal arrays are not copied but just referenced.
- Returns
- invDiffeomorphicMap object
the inverse of this diffeomorphic map.
-
shallow_copy()¶ Shallow copy of this DiffeomorphicMap instance
Creates a shallow copy of this diffeomorphic map (the arrays are not copied but just referenced)
- Returns
- new_mapDiffeomorphicMap object
the shallow copy of this diffeomorphic map
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transform(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)¶ Warps an image in the forward direction
Transforms the input image under this transformation in the forward direction. It uses the “is_inverse” flag to switch between “forward” and “backward” (if is_inverse is False, then transform(…) warps the image forwards, else it warps the image backwards).
- Parameters
- imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2
the image to be warped under this transformation in the forward direction
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used for warping, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_world2gridarray, shape (dim+1, dim+1)
the transformation bringing world (space) coordinates to voxel coordinates of the image given as input
- out_shapearray, shape (dim,)
the number of slices, rows and columns of the desired warped image
- out_grid2worldthe transformation bringing voxel coordinates of the
warped image to physical space
- Returns
- warpedarray, shape = out_shape or self.codomain_shape if None
the warped image under this transformation in the forward direction
Notes
See _warp_forward and _warp_backward documentation for further information.
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transform_inverse(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)¶ Warps an image in the backward direction
Transforms the input image under this transformation in the backward direction. It uses the “is_inverse” flag to switch between “forward” and “backward” (if is_inverse is False, then transform_inverse(…) warps the image backwards, else it warps the image forwards)
- Parameters
- imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2
the image to be warped under this transformation in the forward direction
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used for warping, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_world2gridarray, shape (dim+1, dim+1)
the transformation bringing world (space) coordinates to voxel coordinates of the image given as input
- out_shapearray, shape (dim,)
the number of slices, rows and columns of the desired warped image
- out_grid2worldthe transformation bringing voxel coordinates of the
warped image to physical space
- Returns
- warpedarray, shape = out_shape or self.codomain_shape if None
warped image under this transformation in the backward direction
Notes
See _warp_forward and _warp_backward documentation for further information.
-
warp_endomorphism(phi)¶ Composition of this DiffeomorphicMap with a given endomorphism
Creates a new DiffeomorphicMap C with the same properties as self and composes its displacement fields with phi’s corresponding fields. The resulting diffeomorphism is of the form C(x) = phi(self(x)) with inverse C^{-1}(y) = self^{-1}(phi^{-1}(y)). We assume that phi is an endomorphism with the same discretization and domain affine as self to ensure that the composition inherits self’s properties (we also assume that the pre-aligning matrix of phi is None or identity).
- Parameters
- phiDiffeomorphicMap object
the endomorphism to be warped by this diffeomorphic map
- Returns
- compositionthe composition of this diffeomorphic map with the
endomorphism given as input
Notes
The problem with our current representation of a DiffeomorphicMap is that the set of Diffeomorphism that can be represented this way (a pre-aligning matrix followed by a non-linear endomorphism given as a displacement field) is not closed under the composition operation.
Supporting a general DiffeomorphicMap class, closed under composition, may be extremely costly computationally, and the kind of transformations we actually need for Avants’ mid-point algorithm (SyN) are much simpler.
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EMMetric¶
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class
dipy.workflows.align.EMMetric(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')¶ Bases:
dipy.align.metrics.SimilarityMetricMethods
Computes one step bringing the static image towards the moving.
compute_demons_step([forward_step])Demons step for EM metric
Computes one step bringing the reference image towards the static.
compute_gauss_newton_step([forward_step])Computes the Gauss-Newton energy minimization step
Frees the resources allocated during initialization
The numerical value assigned by this metric to the current image pair
Prepares the metric to compute one displacement field iteration.
set_levels_above(levels)Informs the metric how many pyramid levels are above the current one
set_levels_below(levels)Informs the metric how many pyramid levels are below the current one
set_moving_image(moving_image, …)Sets the moving image being compared against the static one.
set_static_image(static_image, …)Sets the static image being compared against the moving one.
This is called by the optimizer just after setting the moving image.
This is called by the optimizer just after setting the static image.
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__init__(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')¶ Expectation-Maximization Metric
Similarity metric based on the Expectation-Maximization algorithm to handle multi-modal images. The transfer function is modeled as a set of hidden random variables that are estimated at each iteration of the algorithm.
- Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- smoothfloat
smoothness parameter, the larger the value the smoother the deformation field
- inner_iterint
number of iterations to be performed at each level of the multi- resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric)
- q_levelsnumber of quantization levels (equal to the number of hidden
variables in the EM algorithm)
- double_gradientboolean
if True, the gradient of the expected static image under the moving modality will be added to the gradient of the moving image, similarly, the gradient of the expected moving image under the static modality will be added to the gradient of the static image.
- step_typestring (‘gauss_newton’, ‘demons’)
the optimization schedule to be used in the multi-resolution Gauss-Seidel optimization algorithm (not used if Demons Step is selected)
-
compute_backward()¶ Computes one step bringing the static image towards the moving.
Computes the update displacement field to be used for registration of the static image towards the moving image
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compute_demons_step(forward_step=True)¶ Demons step for EM metric
- Parameters
- forward_stepboolean
if True, computes the Demons step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)
- Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Demons step
-
compute_forward()¶ Computes one step bringing the reference image towards the static.
Computes the forward update field to register the moving image towards the static image in a gradient-based optimization algorithm
-
compute_gauss_newton_step(forward_step=True)¶ Computes the Gauss-Newton energy minimization step
Computes the Newton step to minimize this energy, i.e., minimizes the linearized energy function with respect to the regularized displacement field (this step does not require post-smoothing, as opposed to the demons step, which does not include regularization). To accelerate convergence we use the multi-grid Gauss-Seidel algorithm proposed by Bruhn and Weickert et al [Bruhn05]
- Parameters
- forward_stepboolean
if True, computes the Newton step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)
- Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Newton step
References
- [Bruhn05] Andres Bruhn and Joachim Weickert, “Towards ultimate motion
estimation: combining highest accuracy with real-time performance”, 10th IEEE International Conference on Computer Vision, 2005. ICCV 2005.
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free_iteration()¶ Frees the resources allocated during initialization
-
get_energy()¶ The numerical value assigned by this metric to the current image pair
Returns the EM (data term) energy computed at the largest iteration
-
initialize_iteration()¶ Prepares the metric to compute one displacement field iteration.
Pre-computes the transfer functions (hidden random variables) and variances of the estimators. Also pre-computes the gradient of both input images. Note that once the images are transformed to the opposite modality, the gradient of the transformed images can be used with the gradient of the corresponding modality in the same fashion as diff-demons does for mono-modality images. If the flag self.use_double_gradient is True these gradients are averaged.
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use_moving_image_dynamics(original_moving_image, transformation)¶ This is called by the optimizer just after setting the moving image.
EMMetric takes advantage of the image dynamics by computing the current moving image mask from the original_moving_image mask (warped by nearest neighbor interpolation)
- Parameters
- original_moving_imagearray, shape (R, C) or (S, R, C)
the original moving image from which the current moving image was generated, the current moving image is the one that was provided via ‘set_moving_image(…)’, which may not be the same as the original moving image but a warped version of it.
- transformationDiffeomorphicMap object
the transformation that was applied to the original_moving_image to generate the current moving image
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use_static_image_dynamics(original_static_image, transformation)¶ This is called by the optimizer just after setting the static image.
EMMetric takes advantage of the image dynamics by computing the current static image mask from the originalstaticImage mask (warped by nearest neighbor interpolation)
- Parameters
- original_static_imagearray, shape (R, C) or (S, R, C)
the original static image from which the current static image was generated, the current static image is the one that was provided via ‘set_static_image(…)’, which may not be the same as the original static image but a warped version of it (even the static image changes during Symmetric Normalization, not only the moving one).
- transformationDiffeomorphicMap object
the transformation that was applied to the original_static_image to generate the current static image
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ImageRegistrationFlow¶
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class
dipy.workflows.align.ImageRegistrationFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowThe registration workflow is organized as a collection of different functions. The user can intend to use only one type of registration (such as center of mass or rigid body registration only).
Alternatively, a registration can be done in a progressive manner. For example, using affine registration with progressive set to ‘True’ will involve center of mass, translation, rigid body and full affine registration. Whereas, when progressive is False the registration will include only center of mass and affine registration. The progressive registration will be slower but will improve the quality.
This can be controlled by using the progressive flag (True by default).
Methods
affine(static, static_grid2world, moving, …)Function for full affine registration.
center_of_mass(static, static_grid2world, …)Function for the center of mass based image registration.
get_io_iterator()Create an iterator for IO.
get_short_name()Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
perform_transformation(static, …)Function to apply the transformation.
rigid(static, static_grid2world, moving, …)Function for rigid body based image registration.
run(static_img_files, moving_img_files[, …])- Parameters
translate(static, static_grid2world, moving, …)Function for translation based registration.
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__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
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affine(static, static_grid2world, moving, moving_grid2world, affreg, params0, progressive)¶ Function for full affine registration.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
- affregAn object of the image registration class.
- params0array, shape (n,)
parameters from which to start the optimization. If None, the optimization will start at the identity transform. n is the number of parameters of the specified transformation.
- progressiveboolean
Flag to enable or disable the progressive registration. (defa ult True)
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center_of_mass(static, static_grid2world, moving, moving_grid2world)¶ Function for the center of mass based image registration.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
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perform_transformation(static, static_grid2world, moving, moving_grid2world, affreg, params0, transform, affine)¶ Function to apply the transformation.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
- affregAn object of the image registration class.
- params0array, shape (n,)
parameters from which to start the optimization. If None, the optimization will start at the identity transform. n is the number of parameters of the specified transformation.
- transformAn instance of transform type.
- affineAffine matrix to be used as starting affine
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rigid(static, static_grid2world, moving, moving_grid2world, affreg, params0, progressive)¶ Function for rigid body based image registration.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
- affregAn object of the image registration class.
- params0array, shape (n,)
parameters from which to start the optimization. If None, the optimization will start at the identity transform. n is the number of parameters of the specified transformation.
- progressiveboolean
Flag to enable or disable the progressive registration. (defa ult True)
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run(static_img_files, moving_img_files, transform='affine', nbins=32, sampling_prop=None, metric='mi', level_iters=[10000, 1000, 100], sigmas=[3.0, 1.0, 0.0], factors=[4, 2, 1], progressive=True, save_metric=False, out_dir='', out_moved='moved.nii.gz', out_affine='affine.txt', out_quality='quality_metric.txt')¶ - Parameters
- static_img_filesstring
Path to the static image file.
- moving_img_filesstring
Path to the moving image file.
- transformstring, optional
- com: center of mass, trans: translation, rigid: rigid body
affine: full affine including translation, rotation, shearing and scaling (default ‘affine’).
- nbinsint, optional
- Number of bins to discretize the joint and marginal PDF
(default ‘32’).
- sampling_propint, optional
- Number ([0-100]) of voxels for calculating the PDF.
‘None’ implies all voxels (default ‘None’).
- metricstring, optional
- Similarity metric for gathering mutual information
(default ‘mi’ , Mutual Information metric).
- level_itersvariable int, optional
- The number of iterations at each scale of the scale space.
level_iters[0] corresponds to the coarsest scale, level_iters[-1] the finest, where n is the length of the
sequence. By default, a 3-level scale space with iterations sequence equal to [10000, 1000, 100] will be used.
- sigmasvariable floats, optional
- Custom smoothing parameter to build the scale space (one parameter
for each scale). By default, the sequence of sigmas will be [3, 1, 0].
- factorsvariable floats, optional
- Custom scale factors to build the scale space (one factor for each
scale). By default, the sequence of factors will be [4, 2, 1].
- progressiveboolean, optional
Enable/Disable the progressive registration (default ‘True’).
- save_metricboolean, optional
If true, quality assessment metric are saved in ‘quality_metric.txt’ (default ‘False’).
- out_dirstring, optional
- Directory to save the transformed image and the affine matrix
(default ‘’).
- out_movedstring, optional
- Name for the saved transformed image
(default ‘moved.nii.gz’).
- out_affinestring, optional
- Name for the saved affine matrix
(default ‘affine.txt’).
- out_qualitystring, optional
- Name of the file containing the saved quality
metric (default ‘quality_metric.txt’).
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translate(static, static_grid2world, moving, moving_grid2world, affreg, params0)¶ Function for translation based registration.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static image. The default is None, implying the transform is the identity.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving image. The default is None, implying the transform is the identity.
- affregAn object of the image registration class.
- params0array, shape (n,)
parameters from which to start the optimization. If None, the optimization will start at the identity transform. n is the number of parameters of the specified transformation.
MutualInformationMetric¶
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class
dipy.workflows.align.MutualInformationMetric(nbins=32, sampling_proportion=None)¶ Bases:
objectMethods
distance(params)Numeric value of the negative Mutual Information.
distance_and_gradient(params)Numeric value of the metric and its gradient at given parameters.
gradient(params)Numeric value of the metric’s gradient at the given parameters.
setup(transform, static, moving[, …])Prepare the metric to compute intensity densities and gradients.
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__init__(nbins=32, sampling_proportion=None)¶ Initialize an instance of the Mutual Information metric.
This class implements the methods required by Optimizer to drive the registration process.
- Parameters
- nbinsint, optional
the number of bins to be used for computing the intensity histograms. The default is 32.
- sampling_proportionNone or float in interval (0, 1], optional
There are two types of sampling: dense and sparse. Dense sampling uses all voxels for estimating the (joint and marginal) intensity histograms, while sparse sampling uses a subset of them. If sampling_proportion is None, then dense sampling is used. If sampling_proportion is a floating point value in (0,1] then sparse sampling is used, where sampling_proportion specifies the proportion of voxels to be used. The default is None.
Notes
Since we use linear interpolation, images are not, in general, differentiable at exact voxel coordinates, but they are differentiable between voxel coordinates. When using sparse sampling, selected voxels are slightly moved by adding a small random displacement within one voxel to prevent sampling points from being located exactly at voxel coordinates. When using dense sampling, this random displacement is not applied.
-
distance(params)¶ Numeric value of the negative Mutual Information.
We need to change the sign so we can use standard minimization algorithms.
- Parameters
- paramsarray, shape (n,)
the parameter vector of the transform currently used by the metric (the transform name is provided when self.setup is called), n is the number of parameters of the transform
- Returns
- neg_mifloat
the negative mutual information of the input images after transforming the moving image by the currently set transform with params parameters
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distance_and_gradient(params)¶ Numeric value of the metric and its gradient at given parameters.
- Parameters
- paramsarray, shape (n,)
the parameter vector of the transform currently used by the metric (the transform name is provided when self.setup is called), n is the number of parameters of the transform
- Returns
- neg_mifloat
the negative mutual information of the input images after transforming the moving image by the currently set transform with params parameters
- neg_mi_gradarray, shape (n,)
the gradient of the negative Mutual Information
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gradient(params)¶ Numeric value of the metric’s gradient at the given parameters.
- Parameters
- paramsarray, shape (n,)
the parameter vector of the transform currently used by the metric (the transform name is provided when self.setup is called), n is the number of parameters of the transform
- Returns
- gradarray, shape (n,)
the gradient of the negative Mutual Information
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setup(transform, static, moving, static_grid2world=None, moving_grid2world=None, starting_affine=None)¶ Prepare the metric to compute intensity densities and gradients.
The histograms will be setup to compute probability densities of intensities within the minimum and maximum values of static and moving
- Parameters
- transform: instance of Transform
the transformation with respect to whose parameters the gradient must be computed
- staticarray, shape (S, R, C) or (R, C)
static image
- movingarray, shape (S’, R’, C’) or (R’, C’)
moving image. The dimensions of the static (S, R, C) and moving (S’, R’, C’) images do not need to be the same.
- static_grid2worldarray (dim+1, dim+1), optional
the grid-to-space transform of the static image. The default is None, implying the transform is the identity.
- moving_grid2worldarray (dim+1, dim+1)
the grid-to-space transform of the moving image. The default is None, implying the spacing along all axes is 1.
- starting_affinearray, shape (dim+1, dim+1), optional
the pre-aligning matrix (an affine transform) that roughly aligns the moving image towards the static image. If None, no pre-alignment is performed. If a pre-alignment matrix is available, it is recommended to provide this matrix as starting_affine instead of manually transforming the moving image to reduce interpolation artifacts. The default is None, implying no pre-alignment is performed.
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ResliceFlow¶
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class
dipy.workflows.align.ResliceFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, new_vox_size[, order, …])Reslice data with new voxel resolution defined by
new_vox_sz-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
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run(input_files, new_vox_size, order=1, mode='constant', cval=0, num_processes=1, out_dir='', out_resliced='resliced.nii.gz')¶ Reslice data with new voxel resolution defined by
new_vox_sz- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- new_vox_sizevariable float
new voxel size
- orderint, optional
order of interpolation, from 0 to 5, for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don’t want any smoothing 0 is the option you need (default 1)
- modestring, optional
Points outside the boundaries of the input are filled according to the given mode ‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’ (default ‘constant’)
- cvalfloat, optional
Value used for points outside the boundaries of the input if mode=’constant’ (default 0)
- num_processesint, optional
Split the calculation to a pool of children processes. This only applies to 4D data arrays. If a positive integer then it defines the size of the multiprocessing pool that will be used. If 0, then the size of the pool will equal the number of cores available. (default 1)
- out_dirstring, optional
Output directory (default input file directory)
- out_reslicedstring, optional
Name of the resliced dataset to be saved (default ‘resliced.nii.gz’)
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RigidTransform3D¶
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class
dipy.workflows.align.RigidTransform3D¶ Bases:
dipy.align.transforms.TransformMethods
get_identity_parametersParameter values corresponding to the identity transform
jacobianJacobian function of this transform
param_to_matrixMatrix representation of this transform with the given parameters
get_dim
get_number_of_parameters
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__init__()¶ Rigid transform in 3D (rotation + translation) The parameter vector theta of length 6 is interpreted as follows: theta[0] : rotation about the x axis theta[1] : rotation about the y axis theta[2] : rotation about the z axis theta[3] : translation along the x axis theta[4] : translation along the y axis theta[5] : translation along the z axis
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SSDMetric¶
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class
dipy.workflows.align.SSDMetric(dim, smooth=4, inner_iter=10, step_type='demons')¶ Bases:
dipy.align.metrics.SimilarityMetricMethods
Computes one step bringing the static image towards the moving.
compute_demons_step([forward_step])Demons step for SSD metric
Computes one step bringing the reference image towards the static.
compute_gauss_newton_step([forward_step])Computes the Gauss-Newton energy minimization step
Nothing to free for the SSD metric
The numerical value assigned by this metric to the current image pair
Prepares the metric to compute one displacement field iteration.
set_levels_above(levels)Informs the metric how many pyramid levels are above the current one
set_levels_below(levels)Informs the metric how many pyramid levels are below the current one
set_moving_image(moving_image, …)Sets the moving image being compared against the static one.
set_static_image(static_image, …)Sets the static image being compared against the moving one.
use_moving_image_dynamics(…)This is called by the optimizer just after setting the moving image
use_static_image_dynamics(…)This is called by the optimizer just after setting the static image.
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__init__(dim, smooth=4, inner_iter=10, step_type='demons')¶ Sum of Squared Differences (SSD) Metric
Similarity metric for (mono-modal) nonlinear image registration defined by the sum of squared differences (SSD)
- Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- smoothfloat
smoothness parameter, the larger the value the smoother the deformation field
- inner_iterint
number of iterations to be performed at each level of the multi- resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric)
- step_typestring
the displacement field step to be computed when ‘compute_forward’ and ‘compute_backward’ are called. Either ‘demons’ or ‘gauss_newton’
-
compute_backward()¶ Computes one step bringing the static image towards the moving.
Computes the update displacement field to be used for registration of the static image towards the moving image
-
compute_demons_step(forward_step=True)¶ Demons step for SSD metric
Computes the demons step proposed by Vercauteren et al.[Vercauteren09] for the SSD metric.
- Parameters
- forward_stepboolean
if True, computes the Demons step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)
- Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Demons step
References
- [Vercauteren09] Tom Vercauteren, Xavier Pennec, Aymeric Perchant,
Nicholas Ayache, “Diffeomorphic Demons: Efficient Non-parametric Image Registration”, Neuroimage 2009
-
compute_forward()¶ Computes one step bringing the reference image towards the static.
Computes the update displacement field to be used for registration of the moving image towards the static image
-
compute_gauss_newton_step(forward_step=True)¶ Computes the Gauss-Newton energy minimization step
Minimizes the linearized energy function (Newton step) defined by the sum of squared differences of corresponding pixels of the input images with respect to the displacement field.
- Parameters
- forward_stepboolean
if True, computes the Newton step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)
- Returns
- displacementarray, shape = static_image.shape + (3,)
if forward_step==True, the forward SSD Gauss-Newton step, else, the backward step
-
free_iteration()¶ Nothing to free for the SSD metric
-
get_energy()¶ The numerical value assigned by this metric to the current image pair
Returns the Sum of Squared Differences (data term) energy computed at the largest iteration
-
initialize_iteration()¶ Prepares the metric to compute one displacement field iteration.
Pre-computes the gradient of the input images to be used in the computation of the forward and backward steps.
-
SlrWithQbxFlow¶
-
class
dipy.workflows.align.SlrWithQbxFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(static_files, moving_files[, x0, …])Streamline-based linear registration.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(static_files, moving_files, x0='affine', rm_small_clusters=50, qbx_thr=[40, 30, 20, 15], num_threads=None, greater_than=50, less_than=250, nb_pts=20, progressive=True, out_dir='', out_moved='moved.trk', out_affine='affine.txt', out_stat_centroids='static_centroids.trk', out_moving_centroids='moving_centroids.trk', out_moved_centroids='moved_centroids.trk')¶ Streamline-based linear registration.
For efficiency we apply the registration on cluster centroids and remove small clusters.
- Parameters
- static_filesstring
- moving_filesstring
- x0string, optional
rigid, similarity or affine transformation model (default affine)
- rm_small_clustersint, optional
Remove clusters that have less than rm_small_clusters (default 50)
- qbx_thrvariable int, optional
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
- num_threadsint, optional
Number of threads. If None (default) then all available threads will be used. Only metrics using OpenMP will use this variable.
- greater_thanint, optional
Keep streamlines that have length greater than this value (default 50)
- less_thanint, optional
Keep streamlines have length less than this value (default 250)
- np_ptsint, optional
Number of points for discretizing each streamline (default 20)
- progressiveboolean, optional
(default True)
- out_dirstring, optional
Output directory (default input file directory)
- out_movedstring, optional
Filename of moved tractogram (default ‘moved.trk’)
- out_affinestring, optional
Filename of affine for SLR transformation (default ‘affine.txt’)
- out_stat_centroidsstring, optional
Filename of static centroids (default ‘static_centroids.trk’)
- out_moving_centroidsstring, optional
Filename of moving centroids (default ‘moving_centroids.trk’)
- out_moved_centroidsstring, optional
Filename of moved centroids (default ‘moved_centroids.trk’)
Notes
The order of operations is the following. First short or long streamlines are removed. Second the tractogram or a random selection of the tractogram is clustered with QuickBundlesX. Then SLR [Garyfallidis15] is applied.
References
registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015
- Garyfallidis14
Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM, 2014.
- Garyfallidis17
Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration and clustering, NeuroImage, 2017.
-
SymmetricDiffeomorphicRegistration¶
-
class
dipy.workflows.align.SymmetricDiffeomorphicRegistration(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)¶ Bases:
dipy.align.imwarp.DiffeomorphicRegistrationMethods
get_map()Returns the resulting diffeomorphic map Returns the DiffeomorphicMap registering the moving image towards the static image.
optimize(static, moving[, …])Starts the optimization
set_level_iters(level_iters)Sets the number of iterations at each pyramid level
update(current_displacement, …)Composition of the current displacement field with the given field
-
__init__(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)¶ Symmetric Diffeomorphic Registration (SyN) Algorithm
Performs the multi-resolution optimization algorithm for non-linear registration using a given similarity metric.
- Parameters
- metricSimilarityMetric object
the metric to be optimized
- level_iterslist of int
the number of iterations at each level of the Gaussian Pyramid (the length of the list defines the number of pyramid levels to be used)
- opt_tolfloat
the optimization will stop when the estimated derivative of the energy profile w.r.t. time falls below this threshold
- inv_iterint
the number of iterations to be performed by the displacement field inversion algorithm
- step_lengthfloat
the length of the maximum displacement vector of the update displacement field at each iteration
- ss_sigma_factorfloat
parameter of the scale-space smoothing kernel. For example, the std. dev. of the kernel will be factor*(2^i) in the isotropic case where i = 0, 1, …, n_scales is the scale
- inv_tolfloat
the displacement field inversion algorithm will stop iterating when the inversion error falls below this threshold
- callbackfunction(SymmetricDiffeomorphicRegistration)
a function receiving a SymmetricDiffeomorphicRegistration object to be called after each iteration (this optimizer will call this function passing self as parameter)
-
get_map()¶ Returns the resulting diffeomorphic map Returns the DiffeomorphicMap registering the moving image towards the static image.
-
optimize(static, moving, static_grid2world=None, moving_grid2world=None, prealign=None)¶ Starts the optimization
- Parameters
- staticarray, shape (S, R, C) or (R, C)
the image to be used as reference during optimization. The displacement fields will have the same discretization as the static image.
- movingarray, shape (S, R, C) or (R, C)
the image to be used as “moving” during optimization. Since the deformation fields’ discretization is the same as the static image, it is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘prealign’ matrix
- static_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation associated to the static image
- moving_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation associated to the moving image
- prealignarray, shape (dim+1, dim+1)
the affine transformation (operating on the physical space) pre-aligning the moving image towards the static
- Returns
- static_to_refDiffeomorphicMap object
the diffeomorphic map that brings the moving image towards the static one in the forward direction (i.e. by calling static_to_ref.transform) and the static image towards the moving one in the backward direction (i.e. by calling static_to_ref.transform_inverse).
-
update(current_displacement, new_displacement, disp_world2grid, time_scaling)¶ Composition of the current displacement field with the given field
Interpolates new displacement at the locations defined by current_displacement. Equivalently, computes the composition C of the given displacement fields as C(x) = B(A(x)), where A is current_displacement and B is new_displacement. This function is intended to be used with deformation fields of the same sampling (e.g. to be called by a registration algorithm).
- Parameters
- current_displacementarray, shape (R’, C’, 2) or (S’, R’, C’, 3)
the displacement field defining where to interpolate new_displacement
- new_displacementarray, shape (R, C, 2) or (S, R, C, 3)
the displacement field to be warped by current_displacement
- disp_world2gridarray, shape (dim+1, dim+1)
the space-to-grid transform associated with the displacements’ grid (we assume that both displacements are discretized over the same grid)
- time_scalingfloat
scaling factor applied to d2. The effect may be interpreted as moving d1 displacements along a factor (time_scaling) of d2.
- Returns
- updatedarray, shape (the same as new_displacement)
the warped displacement field
- mean_normthe mean norm of all vectors in current_displacement
-
SynRegistrationFlow¶
-
class
dipy.workflows.align.SynRegistrationFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
get_short_name()Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(static_image_files, moving_image_files)- Parameters
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
run(static_image_files, moving_image_files, prealign_file='', inv_static=False, level_iters=[10, 10, 5], metric='cc', mopt_sigma_diff=2.0, mopt_radius=4, mopt_smooth=0.0, mopt_inner_iter=0.0, mopt_q_levels=256, mopt_double_gradient=True, mopt_step_type='', step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, out_dir='', out_warped='warped_moved.nii.gz', out_inv_static='inc_static.nii.gz', out_field='displacement_field.nii.gz')¶ - Parameters
- static_image_filesstring
Path of the static image file.
- moving_image_filesstring
Path to the moving image file.
- prealign_filestring, optional
- The text file containing pre alignment information via an
affine matrix.
- inv_staticboolean, optional
Apply the inverse mapping to the static image (default ‘False’).
- level_itersvariable int, optional
- The number of iterations at each level of the gaussian pyramid.
By default, a 3-level scale space with iterations sequence equal to [10, 10, 5] will be used. The 0-th level corresponds to the finest resolution.
- metricstring, optional
The metric to be used (Default cc, ‘Cross Correlation metric’). metric available: cc (Cross Correlation), ssd (Sum Squared Difference), em (Expectation-Maximization).
- mopt_sigma_difffloat, optional
Metric option applied on Cross correlation (CC). The standard deviation of the Gaussian smoothing kernel to be applied to the update field at each iteration (default 2.0)
- mopt_radiusint, optional
Metric option applied on Cross correlation (CC). the radius of the squared (cubic) neighborhood at each voxel to be considered to compute the cross correlation. (default 4)
- mopt_smoothfloat, optional
Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). Smoothness parameter, the larger the value the smoother the deformation field. (default 1.0 for EM, 4.0 for SSD)
- mopt_inner_iterint, optional
Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). This is number of iterations to be performed at each level of the multi-resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric). Default 5 for EM, 10 for SSD.
- mopt_q_levelsint, optional
Metric option applied on Expectation Maximization (EM). Number of quantization levels (Default: 256 for EM)
- mopt_double_gradientbool, optional
Metric option applied on Expectation Maximization (EM). if True, the gradient of the expected static image under the moving modality will be added to the gradient of the moving image, similarly, the gradient of the expected moving image under the static modality will be added to the gradient of the static image.
- mopt_step_typestring, optional
Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). The optimization schedule to be used in the multi-resolution Gauss-Seidel optimization algorithm (not used if Demons Step is selected). Possible value: (‘gauss_newton’, ‘demons’). default: ‘gauss_newton’ for EM, ‘demons’ for SSD.
- step_lengthfloat, optional
- the length of the maximum displacement vector of the update
displacement field at each iteration.
- ss_sigma_factorfloat, optional
- parameter of the scale-space smoothing kernel. For example, the
std. dev. of the kernel will be factor*(2^i) in the isotropic case where i = 0, 1, …, n_scales is the scale.
- opt_tolfloat, optional
- the optimization will stop when the estimated derivative of the
energy profile w.r.t. time falls below this threshold.
- inv_iterint, optional
- the number of iterations to be performed by the displacement field
inversion algorithm.
- inv_tolfloat, optional
- the displacement field inversion algorithm will stop iterating
when the inversion error falls below this threshold.
- out_dirstring, optional
Directory to save the transformed files (default ‘’).
- out_warpedstring, optional
Name of the warped file. (default ‘warped_moved.nii.gz’).
- out_inv_staticstring, optional
- Name of the file to save the static image after applying the
inverse mapping (default ‘inv_static.nii.gz’).
- out_fieldstring, optional
Name of the file to save the diffeomorphic map. (default ‘displacement_field.nii.gz’)
TranslationTransform3D¶
-
class
dipy.workflows.align.TranslationTransform3D¶ Bases:
dipy.align.transforms.TransformMethods
get_identity_parametersParameter values corresponding to the identity transform
jacobianJacobian function of this transform
param_to_matrixMatrix representation of this transform with the given parameters
get_dim
get_number_of_parameters
-
__init__()¶ Translation transform in 3D
-
Workflow¶
-
class
dipy.workflows.align.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
check_dimensions¶
-
dipy.workflows.align.check_dimensions(static, moving)¶ Check the dimensions of the input images.
- Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- moving: 2D or 3D array
the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix
load_nifti¶
-
dipy.workflows.align.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
reslice¶
-
dipy.workflows.align.reslice(data, affine, zooms, new_zooms, order=1, mode='constant', cval=0, num_processes=1)¶ Reslice data with new voxel resolution defined by
new_zooms- Parameters
- dataarray, shape (I,J,K) or (I,J,K,N)
3d volume or 4d volume with datasets
- affinearray, shape (4,4)
mapping from voxel coordinates to world coordinates
- zoomstuple, shape (3,)
voxel size for (i,j,k) dimensions
- new_zoomstuple, shape (3,)
new voxel size for (i,j,k) after resampling
- orderint, from 0 to 5
order of interpolation for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don’t want any smoothing 0 is the option you need.
- modestring (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’)
Points outside the boundaries of the input are filled according to the given mode.
- cvalfloat
Value used for points outside the boundaries of the input if mode=’constant’.
- num_processesint
Split the calculation to a pool of children processes. This only applies to 4D data arrays. If a positive integer then it defines the size of the multiprocessing pool that will be used. If 0, then the size of the pool will equal the number of cores available.
- Returns
- data2array, shape (I,J,K) or (I,J,K,N)
datasets resampled into isotropic voxel size
- affine2array, shape (4,4)
new affine for the resampled image
Examples
>>> import nibabel as nib >>> from dipy.align.reslice import reslice >>> from dipy.data import get_fnames >>> fimg = get_fnames('aniso_vox') >>> img = nib.load(fimg) >>> data = img.get_data() >>> data.shape == (58, 58, 24) True >>> affine = img.affine >>> zooms = img.header.get_zooms()[:3] >>> zooms (4.0, 4.0, 5.0) >>> new_zooms = (3.,3.,3.) >>> new_zooms (3.0, 3.0, 3.0) >>> data2, affine2 = reslice(data, affine, zooms, new_zooms) >>> data2.shape == (77, 77, 40) True
save_qa_metric¶
-
dipy.workflows.align.save_qa_metric(fname, xopt, fopt)¶ Save Quality Assurance metrics.
- Parameters
- fname: string
File name to save the metric values.
- xopt: numpy array
The metric containing the optimal parameters for image registration.
- fopt: int
The distance between the registered images.
slr_with_qbx¶
-
dipy.workflows.align.slr_with_qbx(static, moving, x0='affine', rm_small_clusters=50, maxiter=100, select_random=None, verbose=False, greater_than=50, less_than=250, qbx_thr=[40, 30, 20, 15], nb_pts=20, progressive=True, rng=None, num_threads=None)¶ Utility function for registering large tractograms.
For efficiency we apply the registration on cluster centroids and remove small clusters.
- Parameters
- staticStreamlines
- movingStreamlines
- x0str
rigid, similarity or affine transformation model (default affine)
- rm_small_clustersint
Remove clusters that have less than rm_small_clusters (default 50)
- select_randomint
If not None select a random number of streamlines to apply clustering Default None.
- verbosebool,
If True then information about the optimization is shown.
- greater_thanint, optional
Keep streamlines that have length greater than this value (default 50)
- less_thanint, optional
Keep streamlines have length less than this value (default 250)
- qbx_thrvariable int
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
- np_ptsint, optional
Number of points for discretizing each streamline (default 20)
- progressiveboolean, optional
(default True)
- rngRandomState
If None creates RandomState in function.
- num_threadsint
Number of threads. If None (default) then all available threads will be used. Only metrics using OpenMP will use this variable.
Notes
The order of operations is the following. First short or long streamlines are removed. Second the tractogram or a random selection of the tractogram is clustered with QuickBundles. Then SLR [Garyfallidis15] is applied.
References
registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015 .. [R890e584ccf15-Garyfallidis14] Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM, 2014.
- Garyfallidis17
Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
transform_centers_of_mass¶
-
dipy.workflows.align.transform_centers_of_mass(static, static_grid2world, moving, moving_grid2world)¶ Transformation to align the center of mass of the input images.
- Parameters
- staticarray, shape (S, R, C)
static image
- static_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation of the static image
- movingarray, shape (S, R, C)
moving image
- moving_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation of the moving image
- Returns
- affine_mapinstance of AffineMap
the affine transformation (translation only, in this case) aligning the center of mass of the moving image towards the one of the static image
transform_streamlines¶
-
dipy.workflows.align.transform_streamlines(streamlines, mat, in_place=False)¶ Apply affine transformation to streamlines
- Parameters
- streamlinesStreamlines
Streamlines object
- matarray, (4, 4)
transformation matrix
- in_placebool
If True then change data in place. Be careful changes input streamlines.
- Returns
- new_streamlinesStreamlines
Sequence transformed 2D ndarrays of shape[-1]==3
IntrospectiveArgumentParser¶
-
class
dipy.workflows.base.IntrospectiveArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)¶ Bases:
argparse.ArgumentParser- Attributes
- optional_parameters
- output_parameters
- positional_parameters
Methods
add_argument(dest, …[, name, name])add_sub_flow_args(sub_flows)Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method.
add_subparsers(**kwargs)add_workflow(workflow)Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method.
error(message)Prints a usage message incorporating the message to stderr and exits.
exit([status, message])format_usage()get_flow_args([args, namespace])Returns the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.
parse_args([args, namespace])print_usage([file])register(registry_name, value, object)set_defaults(**kwargs)add_argument_group
add_description
add_epilogue
add_mutually_exclusive_group
convert_arg_line_to_args
format_help
get_default
parse_known_args
print_help
show_argument
update_argument
-
__init__(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)¶ Augmenting the argument parser to allow automatic creation of arguments from workflows
- Parameters
- progNone
The name of the program (default: sys.argv[0])
- usageNone
A usage message (default: auto-generated from arguments)
- descriptionstr
A description of what the program does
- epilogstr
Text following the argument descriptions
- parentslist
Parsers whose arguments should be copied into this one
- formatter_classobj
HelpFormatter class for printing help messages
- prefix_charsstr
Characters that prefix optional arguments
- fromfile_prefix_charsNone
Characters that prefix files containing additional arguments
- argument_defaultNone
The default value for all arguments
- conflict_handlerstr
String indicating how to handle conflicts
- add_helpbool
Add a -h/-help option
-
add_description()¶
-
add_epilogue()¶
-
add_sub_flow_args(sub_flows)¶ Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. Only the optional input parameters are extracted for these as they are treated as sub workflows.
- Parameters
- sub_flowsarray of dipy.workflows.workflow.Workflow
Workflows to inspect.
- Returns
- sub_flow_optionalsdictionary of all sub workflow optional parameters
-
add_workflow(workflow)¶ Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. Then add these parameters to the current arparser’s own params to parse. If the workflow is of type combined_workflow, the optional input parameters of its sub workflows will also be added.
- Parameters
- workflowdipy.workflows.workflow.Workflow
Workflow from which to infer parameters.
- Returns
- sub_flow_optionalsdictionary of all sub workflow optional parameters
-
get_flow_args(args=None, namespace=None)¶ Returns the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.
-
property
optional_parameters¶
-
property
output_parameters¶
-
property
positional_parameters¶
-
show_argument(dest)¶
-
update_argument(*args, **kargs)¶
CombinedWorkflow¶
-
class
dipy.workflows.combined_workflow.CombinedWorkflow(output_strategy='append', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
get_optionals(flow, **kwargs)Returns the sub flow’s optional arguments merged with those passed as params in kwargs.
get_short_name()Return A short name for the workflow used to subdivide.
Returns a list of tuples (sub flow name, sub flow run method, sub flow short name) to be used in the sub flow parameters extraction.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
run_sub_flow(flow, *args, **kwargs)Runs the sub flow with the optional parameters passed via the command line.
set_sub_flows_optionals(opts)Sets the self._optionals variable with all sub flow arguments that were passed in the commandline.
-
__init__(output_strategy='append', mix_names=False, force=False, skip=False)¶ Workflow that combines multiple workflows. The workflow combined together are referred as sub flows in this class.
-
get_optionals(flow, **kwargs)¶ Returns the sub flow’s optional arguments merged with those passed as params in kwargs.
-
get_sub_runs()¶ Returns a list of tuples (sub flow name, sub flow run method, sub flow short name) to be used in the sub flow parameters extraction.
-
run_sub_flow(flow, *args, **kwargs)¶ Runs the sub flow with the optional parameters passed via the command line. This is a convenience method to make sub flow running more intuitive on the concrete CombinedWorkflow side.
-
set_sub_flows_optionals(opts)¶ Sets the self._optionals variable with all sub flow arguments that were passed in the commandline.
-
Workflow¶
-
class
dipy.workflows.combined_workflow.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
NLMeansFlow¶
-
class
dipy.workflows.denoise.NLMeansFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files[, sigma, out_dir, out_denoised])Workflow wrapping the nlmeans denoising method.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, sigma=0, out_dir='', out_denoised='dwi_nlmeans.nii.gz')¶ Workflow wrapping the nlmeans denoising method.
It applies nlmeans denoise on each file found by ‘globing’
input_filesand saves the results in a directory specified byout_dir.- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- sigmafloat, optional
Sigma parameter to pass to the nlmeans algorithm (default: auto estimation).
- out_dirstring, optional
Output directory (default input file directory)
- out_denoisedstring, optional
Name of the resulting denoised volume (default: dwi_nlmeans.nii.gz)
-
Workflow¶
-
class
dipy.workflows.denoise.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
estimate_sigma¶
-
dipy.workflows.denoise.estimate_sigma(arr, disable_background_masking=False, N=0)¶ Standard deviation estimation from local patches
- Parameters
- arr3D or 4D ndarray
The array to be estimated
- disable_background_maskingbool, default False
If True, uses all voxels for the estimation, otherwise, only non-zeros voxels are used. Useful if the background is masked by the scanner.
- Nint, default 0
Number of coils of the receiver array. Use N = 1 in case of a SENSE reconstruction (Philips scanners) or the number of coils for a GRAPPA reconstruction (Siemens and GE). Use 0 to disable the correction factor, as for example if the noise is Gaussian distributed. See [1] for more information.
- Returns
- sigmandarray
standard deviation of the noise, one estimation per volume.
Notes
This function is the same as manually taking the standard deviation of the background and gives one value for the whole 3D array. It also includes the coil-dependent correction factor of Koay 2006 (see [1], equation 18) with theta = 0. Since this function was introduced in [2] for T1 imaging, it is expected to perform ok on diffusion MRI data, but might oversmooth some regions and leave others un-denoised for spatially varying noise profiles. Consider using
piesno()to estimate sigma instead if visual inaccuracies are apparent in the denoised result.References
scheme for signal extraction from noisy magnitude MR signals. Journal of Magnetic Resonance), 179(2), 317-22.
C., 2008. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images, IEEE Trans. Med. Imaging 27, 425-41.
load_nifti¶
-
dipy.workflows.denoise.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
nlmeans¶
-
dipy.workflows.denoise.nlmeans(arr, sigma, mask=None, patch_radius=1, block_radius=5, rician=True, num_threads=None)¶ Non-local means for denoising 3D and 4D images
- Parameters
- arr3D or 4D ndarray
The array to be denoised
- mask3D ndarray
- sigmafloat or 3D array
standard deviation of the noise estimated from the data
- patch_radiusint
patch size is
2 x patch_radius + 1. Default is 1.- block_radiusint
block size is
2 x block_radius + 1. Default is 5.- ricianboolean
If True the noise is estimated as Rician, otherwise Gaussian noise is assumed.
- num_threadsint
Number of threads. If None (default) then all available threads will be used (all CPU cores).
- Returns
- denoised_arrndarray
the denoised
arrwhich has the same shape asarr.
References
- Descoteaux08
Descoteaux, Maxime and Wiest-Daesslé, Nicolas and Prima, Sylvain and Barillot, Christian and Deriche, Rachid Impact of Rician Adapted Non-Local Means Filtering on HARDI, MICCAI 2008
Reader¶
-
class
dipy.workflows.docstring_parser.Reader(data)¶ Bases:
objectA line-based string reader.
Methods
eof
is_empty
peek
read
read_to_condition
read_to_next_empty_line
read_to_next_unindented_line
reset
seek_next_non_empty_line
-
__init__(data)¶ - Parameters
- datastr
String with lines separated by ‘
- ‘.
-
eof()¶
-
is_empty()¶
-
peek(n=0)¶
-
read()¶
-
read_to_condition(condition_func)¶
-
read_to_next_empty_line()¶
-
read_to_next_unindented_line()¶
-
reset()¶
-
seek_next_non_empty_line()¶
-
dedent_lines¶
-
dipy.workflows.docstring_parser.dedent_lines(lines)¶ Deindent a list of lines maximally
warn¶
-
dipy.workflows.docstring_parser.warn()¶ Issue a warning, or maybe ignore it or raise an exception.
IntrospectiveArgumentParser¶
-
class
dipy.workflows.flow_runner.IntrospectiveArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)¶ Bases:
argparse.ArgumentParser- Attributes
- optional_parameters
- output_parameters
- positional_parameters
Methods
add_argument(dest, …[, name, name])add_sub_flow_args(sub_flows)Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method.
add_subparsers(**kwargs)add_workflow(workflow)Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method.
error(message)Prints a usage message incorporating the message to stderr and exits.
exit([status, message])format_usage()get_flow_args([args, namespace])Returns the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.
parse_args([args, namespace])print_usage([file])register(registry_name, value, object)set_defaults(**kwargs)add_argument_group
add_description
add_epilogue
add_mutually_exclusive_group
convert_arg_line_to_args
format_help
get_default
parse_known_args
print_help
show_argument
update_argument
-
__init__(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)¶ Augmenting the argument parser to allow automatic creation of arguments from workflows
- Parameters
- progNone
The name of the program (default: sys.argv[0])
- usageNone
A usage message (default: auto-generated from arguments)
- descriptionstr
A description of what the program does
- epilogstr
Text following the argument descriptions
- parentslist
Parsers whose arguments should be copied into this one
- formatter_classobj
HelpFormatter class for printing help messages
- prefix_charsstr
Characters that prefix optional arguments
- fromfile_prefix_charsNone
Characters that prefix files containing additional arguments
- argument_defaultNone
The default value for all arguments
- conflict_handlerstr
String indicating how to handle conflicts
- add_helpbool
Add a -h/-help option
-
add_description()¶
-
add_epilogue()¶
-
add_sub_flow_args(sub_flows)¶ Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. Only the optional input parameters are extracted for these as they are treated as sub workflows.
- Parameters
- sub_flowsarray of dipy.workflows.workflow.Workflow
Workflows to inspect.
- Returns
- sub_flow_optionalsdictionary of all sub workflow optional parameters
-
add_workflow(workflow)¶ Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. Then add these parameters to the current arparser’s own params to parse. If the workflow is of type combined_workflow, the optional input parameters of its sub workflows will also be added.
- Parameters
- workflowdipy.workflows.workflow.Workflow
Workflow from which to infer parameters.
- Returns
- sub_flow_optionalsdictionary of all sub workflow optional parameters
-
get_flow_args(args=None, namespace=None)¶ Returns the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.
-
property
optional_parameters¶
-
property
output_parameters¶
-
property
positional_parameters¶
-
show_argument(dest)¶
-
update_argument(*args, **kargs)¶
get_level¶
-
dipy.workflows.flow_runner.get_level(lvl)¶ Transforms the logging level passed on the commandline into a proper logging level name.
run_flow¶
-
dipy.workflows.flow_runner.run_flow(flow)¶ Wraps the process of building an argparser that reflects the workflow that we want to run along with some generic parameters like logging, force and output strategies. The resulting parameters are then fed to the workflow’s run method.
FetchFlow¶
-
class
dipy.workflows.io.FetchFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
load_module(module_path)Load / reload an external module.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(data_names[, out_dir])Download files to folder and check their md5 checksums.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
load_module(module_path)¶ Load / reload an external module.
- Parameters
- module_path: string
the path to the module relative to the main script
- Returns
- module: module object
-
run(data_names, out_dir='')¶ Download files to folder and check their md5 checksums.
- Parameters
- data_namesvariable string
Any number of Nifti1, bvals or bvecs files.
- out_dirstring, optional
Output directory. Default: dipy home folder (~/.dipy)
-
IoInfoFlow¶
-
class
dipy.workflows.io.IoInfoFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files[, b0_threshold, bvecs_tol, …])Provides useful information about different files used in medical imaging.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, b0_threshold=50, bvecs_tol=0.01, bshell_thr=100)¶ Provides useful information about different files used in medical imaging. Any number of input files can be provided. The program identifies the type of file by its extension.
- Parameters
- input_filesvariable string
Any number of Nifti1, bvals or bvecs files.
- b0_thresholdfloat, optional
(default 50)
- bvecs_tolfloat, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors (default 0.01)
- bshell_thrfloat, optional
Threshold for distinguishing b-values in different shells (default 100)
-
Workflow¶
-
class
dipy.workflows.io.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
getfullargspec¶
-
dipy.workflows.io.getfullargspec(func)¶ Get the names and default values of a callable object’s parameters.
A tuple of seven things is returned: (args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations). ‘args’ is a list of the parameter names. ‘varargs’ and ‘varkw’ are the names of the * and ** parameters or None. ‘defaults’ is an n-tuple of the default values of the last n parameters. ‘kwonlyargs’ is a list of keyword-only parameter names. ‘kwonlydefaults’ is a dictionary mapping names from kwonlyargs to defaults. ‘annotations’ is a dictionary mapping parameter names to annotations.
- Notable differences from inspect.signature():
the “self” parameter is always reported, even for bound methods
wrapper chains defined by __wrapped__ not unwrapped automatically
getmembers¶
-
dipy.workflows.io.getmembers(object, predicate=None)¶ Return all members of an object as (name, value) pairs sorted by name. Optionally, only return members that satisfy a given predicate.
isfunction¶
-
dipy.workflows.io.isfunction(object)¶ Return true if the object is a user-defined function.
- Function objects provide these attributes:
__doc__ documentation string __name__ name with which this function was defined __code__ code object containing compiled function bytecode __defaults__ tuple of any default values for arguments __globals__ global namespace in which this function was defined __annotations__ dict of parameter annotations __kwdefaults__ dict of keyword only parameters with defaults
load_nifti¶
-
dipy.workflows.io.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
MaskFlow¶
-
class
dipy.workflows.mask.MaskFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, lb[, ub, out_dir, out_mask])Workflow for creating a binary mask
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, lb, ub=inf, out_dir='', out_mask='mask.nii.gz')¶ Workflow for creating a binary mask
- Parameters
- input_filesstring
Path to image to be masked.
- lbfloat
Lower bound value.
- ubfloat, optional
Upper bound value (default Inf)
- out_dirstring, optional
Output directory (default input file directory)
- out_maskstring, optional
Name of the masked file (default ‘mask.nii.gz’)
-
Workflow¶
-
class
dipy.workflows.mask.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
load_nifti¶
-
dipy.workflows.mask.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
IOIterator¶
-
class
dipy.workflows.multi_io.IOIterator(output_strategy='absolute', mix_names=False)¶ Bases:
objectCreate output filenames that work nicely with multiple input files from multiple directories (processing multiple subjects with one command)
Use information from input files, out_dir and out_fnames to generate correct outputs which can come from long lists of multiple or single inputs.
Methods
create_directories
create_outputs
file_existence_check
set_inputs
set_out_dir
set_out_fnames
set_output_keys
-
__init__(output_strategy='absolute', mix_names=False)¶ Initialize self. See help(type(self)) for accurate signature.
-
create_directories()¶
-
create_outputs()¶
-
file_existence_check(args)¶
-
set_inputs(*args)¶
-
set_out_dir(out_dir)¶
-
set_out_fnames(*args)¶
-
set_output_keys(*args)¶
-
common_start¶
-
dipy.workflows.multi_io.common_start(sa, sb)¶ Return the longest common substring from the beginning of sa and sb.
concatenate_inputs¶
-
dipy.workflows.multi_io.concatenate_inputs(multi_inputs)¶ Concatenate list of inputs
connect_output_paths¶
-
dipy.workflows.multi_io.connect_output_paths(inputs, out_dir, out_files, output_strategy='absolute', mix_names=True)¶ Generates a list of output files paths based on input files and output strategies.
- Parameters
- inputsarray
List of input paths.
- out_dirstring
The output directory.
- out_filesarray
List of output files.
- output_strategystring
- Which strategy to use to generate the output paths.
‘append’: Add out_dir to the path of the input. ‘prepend’: Add the input path directory tree to out_dir. ‘absolute’: Put directly in out_dir.
- mix_namesbool
Whether or not prepend a string composed of a mix of the input names to the final output name.
- Returns
- A list of output file paths.
glob¶
-
dipy.workflows.multi_io.glob(pathname, *, recursive=False)¶ Return a list of paths matching a pathname pattern.
The pattern may contain simple shell-style wildcards a la fnmatch. However, unlike fnmatch, filenames starting with a dot are special cases that are not matched by ‘*’ and ‘?’ patterns.
If recursive is true, the pattern ‘**’ will match any files and zero or more directories and subdirectories.
io_iterator¶
-
dipy.workflows.multi_io.io_iterator(inputs, out_dir, fnames, output_strategy='absolute', mix_names=False, out_keys=None)¶ Creates an IOIterator from the parameters.
- Parameters
- inputsarray
List of input files.
- out_dirstring
Output directory.
- fnamesarray
File names of all outputs to be created.
- output_strategystring
Controls the behavior of the IOIterator for output paths.
- mix_namesbool
Whether or not to append a mix of input names at the beginning.
- Returns
- ——-
Properly instantiated IOIterator object.
io_iterator_¶
-
dipy.workflows.multi_io.io_iterator_(frame, fnc, output_strategy='absolute', mix_names=False)¶ Creates an IOIterator using introspection.
- Parameters
- frameframeobject
Contains the info about the current local variables values.
- fncfunction
The function to inspect
- output_strategystring
Controls the behavior of the IOIterator for output paths.
- mix_namesbool
Whether or not to append a mix of input names at the beginning.
- Returns
- ——-
Properly instantiated IOIterator object.
ConstrainedSphericalDeconvModel¶
-
class
dipy.workflows.reconst.ConstrainedSphericalDeconvModel(gtab, response, reg_sphere=None, sh_order=8, lambda_=1, tau=0.1, convergence=50)¶ Bases:
dipy.reconst.shm.SphHarmModelMethods
cache_clear()Clear the cache.
cache_get(tag, key[, default])Retrieve a value from the cache.
cache_set(tag, key, value)Store a value in the cache.
fit(data[, mask])Fit method for every voxel in data
predict(sh_coeff[, gtab, S0])Compute a signal prediction given spherical harmonic coefficients for the provided GradientTable class instance.
sampling_matrix(sphere)The matrix needed to sample ODFs from coefficients of the model.
-
__init__(gtab, response, reg_sphere=None, sh_order=8, lambda_=1, tau=0.1, convergence=50)¶ Constrained Spherical Deconvolution (CSD) [1].
Spherical deconvolution computes a fiber orientation distribution (FOD), also called fiber ODF (fODF) [2], as opposed to a diffusion ODF as the QballModel or the CsaOdfModel. This results in a sharper angular profile with better angular resolution that is the best object to be used for later deterministic and probabilistic tractography [3].
A sharp fODF is obtained because a single fiber response function is injected as a priori knowledge. The response function is often data-driven and is thus provided as input to the ConstrainedSphericalDeconvModel. It will be used as deconvolution kernel, as described in [1].
- Parameters
- gtabGradientTable
- responsetuple or AxSymShResponse object
A tuple with two elements. The first is the eigen-values as an (3,) ndarray and the second is the signal value for the response function without diffusion weighting (i.e. S0). This is to be able to generate a single fiber synthetic signal. The response function will be used as deconvolution kernel ([1]).
- reg_sphereSphere (optional)
sphere used to build the regularization B matrix. Default: ‘symmetric362’.
- sh_orderint (optional)
maximal spherical harmonics order. Default: 8
- lambda_float (optional)
weight given to the constrained-positivity regularization part of the deconvolution equation (see [1]). Default: 1
- taufloat (optional)
threshold controlling the amplitude below which the corresponding fODF is assumed to be zero. Ideally, tau should be set to zero. However, to improve the stability of the algorithm, tau is set to tau*100 % of the mean fODF amplitude (here, 10% by default) (see [1]). Default: 0.1
- convergenceint
Maximum number of iterations to allow the deconvolution to converge.
References
- 1(1,2,3,4,5,6)
Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution
- 2(1,2)
Descoteaux, M., et al. IEEE TMI 2009. Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions
- 3(1,2)
Côté, M-A., et al. Medical Image Analysis 2013. Tractometer: Towards validation of tractography pipelines
- 4
Tournier, J.D, et al. Imaging Systems and Technology 2012. MRtrix: Diffusion Tractography in Crossing Fiber Regions
-
fit(data, mask=None)¶ Fit method for every voxel in data
-
predict(sh_coeff, gtab=None, S0=1.0)¶ Compute a signal prediction given spherical harmonic coefficients for the provided GradientTable class instance.
- Parameters
- sh_coeffndarray
The spherical harmonic representation of the FOD from which to make the signal prediction.
- gtabGradientTable
The gradients for which the signal will be predicted. Use the model’s gradient table by default.
- S0ndarray or float
The non diffusion-weighted signal value.
- Returns
- pred_signdarray
The predicted signal.
-
CsaOdfModel¶
-
class
dipy.workflows.reconst.CsaOdfModel(gtab, sh_order, smooth=0.006, min_signal=1e-05, assume_normed=False)¶ Bases:
dipy.reconst.shm.QballBaseModelImplementation of Constant Solid Angle reconstruction method.
References
- 1
Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging With Solid Angle Consideration.
Methods
cache_clear()Clear the cache.
cache_get(tag, key[, default])Retrieve a value from the cache.
cache_set(tag, key, value)Store a value in the cache.
fit(data[, mask])Fits the model to diffusion data and returns the model fit
sampling_matrix(sphere)The matrix needed to sample ODFs from coefficients of the model.
-
__init__(gtab, sh_order, smooth=0.006, min_signal=1e-05, assume_normed=False)¶ Creates a model that can be used to fit or sample diffusion data
- Parameters
- gtabGradientTable
Diffusion gradients used to acquire data
- sh_ordereven int >= 0
the spherical harmonic order of the model
- smoothfloat between 0 and 1, optional
The regularization parameter of the model
- min_signalfloat, > 0, optional
During fitting, all signal values less than min_signal are clipped to min_signal. This is done primarily to avoid values less than or equal to zero when taking logs.
- assume_normedbool, optional
If True, clipping and normalization of the data with respect to the mean B0 signal are skipped during mode fitting. This is an advanced feature and should be used with care.
See also
normalize_data
-
max= 0.999¶
-
min= 0.001¶
DiffusionKurtosisModel¶
-
class
dipy.workflows.reconst.DiffusionKurtosisModel(gtab, fit_method='WLS', *args, **kwargs)¶ Bases:
dipy.reconst.base.ReconstModelClass for the Diffusion Kurtosis Model
Methods
fit(data[, mask])Fit method of the DKI model class
predict(dki_params[, S0])Predict a signal for this DKI model class instance given parameters.
-
__init__(gtab, fit_method='WLS', *args, **kwargs)¶ Diffusion Kurtosis Tensor Model [1]
- Parameters
- gtabGradientTable class instance
- fit_methodstr or callable
str can be one of the following: ‘OLS’ or ‘ULLS’ for ordinary least squares
dki.ols_fit_dki
- args, kwargsarguments and key-word arguments passed to the
fit_method. See dki.ols_fit_dki, dki.wls_fit_dki for details
References
- 1
Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med. 65(3), 823-836
-
fit(data, mask=None)¶ Fit method of the DKI model class
- Parameters
- dataarray
The measured signal from one voxel.
- maskarray
A boolean array used to mark the coordinates in the data that should be analyzed that has the shape data.shape[-1]
-
predict(dki_params, S0=1.0)¶ Predict a signal for this DKI model class instance given parameters.
- Parameters
- dki_paramsndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model. Parameters are ordered as follows:
Three diffusion tensor’s eigenvalues
Three lines of the eigenvector matrix each containing the first, second and third coordinates of the eigenvector
Fifteen elements of the kurtosis tensor
- S0float or ndarray (optional)
The non diffusion-weighted signal in every voxel, or across all voxels. Default: 1
-
ReconstCSAFlow¶
-
class
dipy.workflows.reconst.ReconstCSAFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, bvalues_files, …[, …])Constant Solid Angle.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, bvalues_files, bvectors_files, mask_files, sh_order=6, odf_to_sh_order=8, b0_threshold=50.0, bvecs_tol=0.01, extract_pam_values=False, parallel=False, nbr_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')¶ Constant Solid Angle.
- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- bvalues_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- bvectors_filesstring
Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.
- mask_filesstring
Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)
- sh_orderint, optional
Spherical harmonics order (default 6) used in the CSA fit.
- odf_to_sh_orderint, optional
Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients (default 8)
- b0_thresholdfloat, optional
Threshold used to find b=0 directions
- bvecs_tolfloat, optional
Threshold used so that norm(bvec)=1 (default 0.01)
- extract_pam_valuesbool, optional
Wheter or not to save pam volumes as single nifti files.
- parallelbool, optional
Whether to use parallelization in peak-finding during the calibration procedure. Default: False
- nbr_processesint, optional
If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()).
- out_dirstring, optional
Output directory (default input file directory)
- out_pamstring, optional
Name of the peaks volume to be saved (default ‘peaks.pam5’)
- out_shmstring, optional
Name of the spherical harmonics volume to be saved (default ‘shm.nii.gz’)
- out_peaks_dirstring, optional
Name of the peaks directions volume to be saved (default ‘peaks_dirs.nii.gz’)
- out_peaks_valuesstring, optional
Name of the peaks values volume to be saved (default ‘peaks_values.nii.gz’)
- out_peaks_indicesstring, optional
Name of the peaks indices volume to be saved (default ‘peaks_indices.nii.gz’)
- out_gfastring, optional
Name of the generalized FA volume to be saved (default ‘gfa.nii.gz’)
References
- 1
Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging with Solid Angle Consideration.
-
ReconstCSDFlow¶
-
class
dipy.workflows.reconst.ReconstCSDFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, bvalues_files, …[, …])Constrained spherical deconvolution
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50.0, bvecs_tol=0.01, roi_center=None, roi_radius=10, fa_thr=0.7, frf=None, extract_pam_values=False, sh_order=8, odf_to_sh_order=8, parallel=False, nbr_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')¶ Constrained spherical deconvolution
- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- bvalues_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- bvectors_filesstring
Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.
- mask_filesstring
Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)
- b0_thresholdfloat, optional
Threshold used to find b=0 directions
- bvecs_tolfloat, optional
Bvecs should be unit vectors. (default:0.01)
- roi_centervariable int, optional
Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3] (default None)
- roi_radiusint, optional
radius of cubic ROI in voxels (default 10)
- fa_thrfloat, optional
FA threshold for calculating the response function (default 0.7)
- frfvariable float, optional
Fiber response function can be for example inputed as 15 4 4 (from the command line) or [15, 4, 4] from a Python script to be converted to float and multiplied by 10**-4 . If None the fiber response function will be computed automatically (default: None).
- extract_pam_valuesbool, optional
Save or not to save pam volumes as single nifti files.
- sh_orderint, optional
Spherical harmonics order (default 6) used in the CSA fit.
- odf_to_sh_orderint, optional
Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients (default 8)
- parallelbool, optional
Whether to use parallelization in peak-finding during the calibration procedure. Default: False
- nbr_processesint, optional
If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()).
- out_dirstring, optional
Output directory (default input file directory)
- out_pamstring, optional
Name of the peaks volume to be saved (default ‘peaks.pam5’)
- out_shmstring, optional
Name of the spherical harmonics volume to be saved (default ‘shm.nii.gz’)
- out_peaks_dirstring, optional
Name of the peaks directions volume to be saved (default ‘peaks_dirs.nii.gz’)
- out_peaks_valuesstring, optional
Name of the peaks values volume to be saved (default ‘peaks_values.nii.gz’)
- out_peaks_indicesstring, optional
Name of the peaks indices volume to be saved (default ‘peaks_indices.nii.gz’)
- out_gfastring, optional
Name of the generalized FA volume to be saved (default ‘gfa.nii.gz’)
References
- 1
Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution.
-
ReconstDkiFlow¶
-
class
dipy.workflows.reconst.ReconstDkiFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, bvalues_files, …[, …])Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics.
get_dki_model
get_fitted_tensor
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_dki_model(gtab)¶
-
get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50)¶
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50.0, save_metrics=[], out_dir='', out_dt_tensor='dti_tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', out_dk_tensor='dki_tensors.nii.gz', out_mk='mk.nii.gz', out_ak='ak.nii.gz', out_rk='rk.nii.gz')¶ Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics. Performs a DKI reconstruction on the files by ‘globing’
input_filesand saves the DKI metrics in a directory specified byout_dir.- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- bvalues_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- bvectors_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- mask_filesstring
Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)
- b0_thresholdfloat, optional
Threshold used to find b=0 directions (default 0.0)
- save_metricsvariable string, optional
List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval (default [] (all))
- out_dirstring, optional
Output directory (default input file directory)
- out_dt_tensorstring, optional
Name of the tensors volume to be saved (default: ‘dti_tensors.nii.gz’)
- out_dk_tensorstring, optional
Name of the tensors volume to be saved (default ‘dki_tensors.nii.gz’)
- out_fastring, optional
Name of the fractional anisotropy volume to be saved (default ‘fa.nii.gz’)
- out_gastring, optional
Name of the geodesic anisotropy volume to be saved (default ‘ga.nii.gz’)
- out_rgbstring, optional
Name of the color fa volume to be saved (default ‘rgb.nii.gz’)
- out_mdstring, optional
Name of the mean diffusivity volume to be saved (default ‘md.nii.gz’)
- out_adstring, optional
Name of the axial diffusivity volume to be saved (default ‘ad.nii.gz’)
- out_rdstring, optional
Name of the radial diffusivity volume to be saved (default ‘rd.nii.gz’)
- out_modestring, optional
Name of the mode volume to be saved (default ‘mode.nii.gz’)
- out_evecstring, optional
Name of the eigenvectors volume to be saved (default ‘evecs.nii.gz’)
- out_evalstring, optional
Name of the eigenvalues to be saved (default ‘evals.nii.gz’)
- out_mkstring, optional
Name of the mean kurtosis to be saved (default: ‘mk.nii.gz’)
- out_akstring, optional
Name of the axial kurtosis to be saved (default: ‘ak.nii.gz’)
- out_rkstring, optional
Name of the radial kurtosis to be saved (default: ‘rk.nii.gz’)
References
- 1
Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med. 65(3), 823-836
- 2
Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu, and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The Quantification of Non-Gaussian Water Diffusion by Means of Magnetic Resonance Imaging. MRM 53 (6):1432-40.
-
ReconstDtiFlow¶
-
class
dipy.workflows.reconst.ReconstDtiFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, bvalues_files, …[, …])Workflow for tensor reconstruction and for computing DTI metrics.
get_fitted_tensor
get_tensor_model
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50, bvecs_tol=0.01)¶
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_tensor_model(gtab)¶
-
run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50, bvecs_tol=0.01, save_metrics=[], out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', nifti_tensor=True)¶ Workflow for tensor reconstruction and for computing DTI metrics. using Weighted Least-Squares. Performs a tensor reconstruction on the files by ‘globing’
input_filesand saves the DTI metrics in a directory specified byout_dir.- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- bvalues_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- bvectors_filesstring
Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.
- mask_filesstring
Path to the input masks. This path may contain wildcards to use multiple masks at once.
- b0_thresholdfloat, optional
Threshold used to find b=0 directions (default 0.0)
- bvecs_tolfloat, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors (default 0.01)
- save_metricsvariable string, optional
List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval (default [] (all))
- out_dirstring, optional
Output directory (default input file directory)
- out_tensorstring, optional
Name of the tensors volume to be saved (default ‘tensors.nii.gz’). Per default, this will be saved following the nifti standard: with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If nifti_tensor is False, this will be saved in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.
- out_fastring, optional
Name of the fractional anisotropy volume to be saved (default ‘fa.nii.gz’)
- out_gastring, optional
Name of the geodesic anisotropy volume to be saved (default ‘ga.nii.gz’)
- out_rgbstring, optional
Name of the color fa volume to be saved (default ‘rgb.nii.gz’)
- out_mdstring, optional
Name of the mean diffusivity volume to be saved (default ‘md.nii.gz’)
- out_adstring, optional
Name of the axial diffusivity volume to be saved (default ‘ad.nii.gz’)
- out_rdstring, optional
Name of the radial diffusivity volume to be saved (default ‘rd.nii.gz’)
- out_modestring, optional
Name of the mode volume to be saved (default ‘mode.nii.gz’)
- out_evecstring, optional
Name of the eigenvectors volume to be saved (default ‘evecs.nii.gz’)
- out_evalstring, optional
Name of the eigenvalues to be saved (default ‘evals.nii.gz’)
- nifti_tensorbool, optional
Whether the tensor is saved in the standard Nifti format or in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension. Default: True
References
- 1
Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254.
- 2
Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
- 3
Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095
- 4
hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541.
-
ReconstIvimFlow¶
-
class
dipy.workflows.reconst.ReconstIvimFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files, bvalues_files, …[, …])Workflow for Intra-voxel Incoherent Motion reconstruction and for computing IVIM metrics.
get_fitted_ivim
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_fitted_ivim(data, mask, bval, bvec, b0_threshold=50)¶
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, bvalues_files, bvectors_files, mask_files, split_b_D=400, split_b_S0=200, b0_threshold=0, save_metrics=[], out_dir='', out_S0_predicted='S0_predicted.nii.gz', out_perfusion_fraction='perfusion_fraction.nii.gz', out_D_star='D_star.nii.gz', out_D='D.nii.gz')¶ Workflow for Intra-voxel Incoherent Motion reconstruction and for computing IVIM metrics. Performs a IVIM reconstruction on the files by ‘globing’
input_filesand saves the IVIM metrics in a directory specified byout_dir.- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- bvalues_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- bvectors_filesstring
Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.
- mask_filesstring
Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)
- split_b_Dint, optional
Value to split the bvals to estimate D for the two-stage process of fitting (default 400)
- split_b_S0int, optional
Value to split the bvals to estimate S0 for the two-stage process of fitting. (default 200)
- b0_thresholdint, optional
Threshold value for the b0 bval. (default 0)
- save_metricsvariable string, optional
List of metrics to save. Possible values: S0_predicted, perfusion_fraction, D_star, D (default [] (all))
- out_dirstring, optional
Output directory (default input file directory)
- out_S0_predictedstring, optional
Name of the S0 signal estimated to be saved (default: ‘S0_predicted.nii.gz’)
- out_perfusion_fractionstring, optional
Name of the estimated volume fractions to be saved (default ‘perfusion_fraction.nii.gz’)
- out_D_starstring, optional
Name of the estimated pseudo-diffusion parameter to be saved (default ‘D_star.nii.gz’)
- out_Dstring, optional
Name of the estimated diffusion parameter to be saved (default ‘D.nii.gz’)
References
- Stejskal65
Stejskal, E. O.; Tanner, J. E. (1 January 1965). “Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient”. The Journal of Chemical Physics 42 (1): 288. Bibcode: 1965JChPh..42..288S. doi:10.1063/1.1695690.
- LeBihan84
Le Bihan, Denis, et al. “Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging.” Radiology 168.2 (1988): 497-505.
-
ReconstMAPMRIFlow¶
-
class
dipy.workflows.reconst.ReconstMAPMRIFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(data_files, bvals_files, bvecs_files, …)Workflow for fitting the MAPMRI model (with optional Laplacian regularization).
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(data_files, bvals_files, bvecs_files, small_delta, big_delta, b0_threshold=50.0, laplacian=True, positivity=True, bval_threshold=2000, save_metrics=[], laplacian_weighting=0.05, radial_order=6, out_dir='', out_rtop='rtop.nii.gz', out_lapnorm='lapnorm.nii.gz', out_msd='msd.nii.gz', out_qiv='qiv.nii.gz', out_rtap='rtap.nii.gz', out_rtpp='rtpp.nii.gz', out_ng='ng.nii.gz', out_perng='perng.nii.gz', out_parng='parng.nii.gz')¶ Workflow for fitting the MAPMRI model (with optional Laplacian regularization). Generates rtop, lapnorm, msd, qiv, rtap, rtpp, non-gaussian (ng), parallel ng, perpendicular ng saved in a nifti format in input files provided by data_files and saves the nifti files to an output directory specified by out_dir.
In order for the MAPMRI workflow to work in the way intended either the Laplacian or positivity or both must be set to True.
- Parameters
- data_filesstring
Path to the input volume.
- bvals_filesstring
Path to the bval files.
- bvecs_filesstring
Path to the bvec files.
- small_deltafloat
Small delta value used in generation of gradient table of provided bval and bvec.
- big_deltafloat
Big delta value used in generation of gradient table of provided bval and bvec.
- b0_thresholdfloat, optional
Threshold used to find b=0 directions (default 0.0)
- laplacianbool, optional
Regularize using the Laplacian of the MAP-MRI basis (default True)
- positivitybool, optional
Constrain the propagator to be positive. (default True)
- bval_thresholdfloat, optional
Sets the b-value threshold to be used in the scale factor estimation. In order for the estimated non-Gaussianity to have meaning this value should set to a lower value (b<2000 s/mm^2) such that the scale factors are estimated on signal points that reasonably represent the spins at Gaussian diffusion. (default: 2000)
- save_metricsvariable string, optional
List of metrics to save. Possible values: rtop, laplacian_signal, msd, qiv, rtap, rtpp, ng, perng, parng (default: [] (all))
- laplacian_weightingfloat, optional
Weighting value used in fitting the MAPMRI model in the Laplacian and both model types. (default: 0.05)
- radial_orderunsigned int, optional
Even value used to set the order of the basis (default: 6)
- out_dirstring, optional
Output directory (default: input file directory)
- out_rtopstring, optional
Name of the rtop to be saved
- out_lapnormstring, optional
Name of the norm of Laplacian signal to be saved
- out_msdstring, optional
Name of the msd to be saved
- out_qivstring, optional
Name of the qiv to be saved
- out_rtapstring, optional
Name of the rtap to be saved
- out_rtppstring, optional
Name of the rtpp to be saved
- out_ngstring, optional
Name of the Non-Gaussianity to be saved
- out_perngstring, optional
Name of the Non-Gaussianity perpendicular to be saved
- out_parngstring, optional
Name of the Non-Gaussianity parallel to be saved
-
TensorModel¶
-
class
dipy.workflows.reconst.TensorModel(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)¶ Bases:
dipy.reconst.base.ReconstModelDiffusion Tensor
Methods
fit(data[, mask])Fit method of the DTI model class
predict(dti_params[, S0])Predict a signal for this TensorModel class instance given parameters.
-
__init__(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)¶ A Diffusion Tensor Model [1], [2].
- Parameters
- gtabGradientTable class instance
- fit_methodstr or callable
str can be one of the following:
- ‘WLS’ for weighted least squares
dti.wls_fit_tensor()- ‘LS’ or ‘OLS’ for ordinary least squares
dti.ols_fit_tensor()- ‘NLLS’ for non-linear least-squares
dti.nlls_fit_tensor()- ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor
fitting [3]
dti.restore_fit_tensor()- callable has to have the signature:
- return_S0_hatbool
Boolean to return (True) or not (False) the S0 values for the fit.
- args, kwargsarguments and key-word arguments passed to the
fit_method. See dti.wls_fit_tensor, dti.ols_fit_tensor for details
- min_signalfloat
The minimum signal value. Needs to be a strictly positive number. Default: minimal signal in the data provided to fit.
Notes
In order to increase speed of processing, tensor fitting is done simultaneously over many voxels. Many fit_methods use the ‘step’ parameter to set the number of voxels that will be fit at once in each iteration. This is the chunk size as a number of voxels. A larger step value should speed things up, but it will also take up more memory. It is advisable to keep an eye on memory consumption as this value is increased.
E.g., in
iter_fit_tensor()we have a default step value of 1e4References
- 1(1,2)
Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254.
- 2(1,2)
Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
- 3(1,2)
Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095
-
fit(data, mask=None)¶ Fit method of the DTI model class
- Parameters
- dataarray
The measured signal from one voxel.
- maskarray
A boolean array used to mark the coordinates in the data that should be analyzed that has the shape data.shape[:-1]
-
predict(dti_params, S0=1.0)¶ Predict a signal for this TensorModel class instance given parameters.
- Parameters
- dti_paramsndarray
The last dimension should have 12 tensor parameters: 3 eigenvalues, followed by the 3 eigenvectors
- S0float or ndarray
The non diffusion-weighted signal in every voxel, or across all voxels. Default: 1
-
Workflow¶
-
class
dipy.workflows.reconst.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
IvimModel¶
-
dipy.workflows.reconst.IvimModel(gtab, fit_method='trr', **kwargs)¶ Selector function to switch between the 2-stage Trust-Region Reflective based NLLS fitting method (also containing the linear fit): trr and the Variable Projections based fitting method: varpro.
- Parameters
- fit_methodstring, optional
The value fit_method can either be ‘trr’ or ‘varpro’. default : trr
auto_response¶
-
dipy.workflows.reconst.auto_response(gtab, data, roi_center=None, roi_radius=10, fa_thr=0.7, fa_callable=<function fa_superior>, return_number_of_voxels=False)¶ Automatic estimation of response function using FA.
- Parameters
- gtabGradientTable
- datandarray
diffusion data
- roi_centertuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3].
- roi_radiusint
radius of cubic ROI
- fa_thrfloat
FA threshold
- fa_callablecallable
A callable that defines an operation that compares FA with the fa_thr. The operator should have two positional arguments (e.g., fa_operator(FA, fa_thr)) and it should return a bool array.
- return_number_of_voxelsbool
If True, returns the number of voxels used for estimating the response function.
- Returns
- responsetuple, (2,)
(evals, S0)
- ratiofloat
The ratio between smallest versus largest eigenvalue of the response.
- number of voxelsint (optional)
The number of voxels used for estimating the response function.
Notes
In CSD there is an important pre-processing step: the estimation of the fiber response function. In order to do this we look for voxels with very anisotropic configurations. For example we can use an ROI (20x20x20) at the center of the volume and store the signal values for the voxels with FA values higher than 0.7. Of course, if we haven’t precalculated FA we need to fit a Tensor model to the datasets. Which is what we do in this function.
For the response we also need to find the average S0 in the ROI. This is possible using gtab.b0s_mask() we can find all the S0 volumes (which correspond to b-values equal 0) in the dataset.
The response consists always of a prolate tensor created by averaging the highest and second highest eigenvalues in the ROI with FA higher than threshold. We also include the average S0s.
We also return the ratio which is used for the SDT models. If requested, the number of voxels used for estimating the response function is also returned, which can be used to judge the fidelity of the response function. As a rule of thumb, at least 300 voxels should be used to estimate a good response function (see [1]).
References
fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution
axial_diffusivity¶
-
dipy.workflows.reconst.axial_diffusivity(evals, axis=-1)¶ Axial Diffusivity (AD) of a diffusion tensor. Also called parallel diffusivity.
- Parameters
- evalsarray-like
Eigenvalues of a diffusion tensor, must be sorted in descending order along axis.
- axisint
Axis of evals which contains 3 eigenvalues.
- Returns
- adarray
Calculated AD.
Notes
AD is calculated with the following equation:
\[AD = \lambda_1\]
color_fa¶
-
dipy.workflows.reconst.color_fa(fa, evecs)¶ Color fractional anisotropy of diffusion tensor
- Parameters
- faarray-like
Array of the fractional anisotropy (can be 1D, 2D or 3D)
- evecsarray-like
eigen vectors from the tensor model
- Returns
- rgbArray with 3 channels for each color as the last dimension.
Colormap of the FA with red for the x value, y for the green value and z for the blue value.
ec{e})) imes fa
fractional_anisotropy¶
-
dipy.workflows.reconst.fractional_anisotropy(evals, axis=-1)¶ Fractional anisotropy (FA) of a diffusion tensor.
- Parameters
- evalsarray-like
Eigenvalues of a diffusion tensor.
- axisint
Axis of evals which contains 3 eigenvalues.
- Returns
- faarray
Calculated FA. Range is 0 <= FA <= 1.
Notes
FA is calculated using the following equation:
\[FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1- \lambda_3)^2+(\lambda_2-\lambda_3)^2}{\lambda_1^2+ \lambda_2^2+\lambda_3^2}}\]
geodesic_anisotropy¶
-
dipy.workflows.reconst.geodesic_anisotropy(evals, axis=-1)¶ Geodesic anisotropy (GA) of a diffusion tensor.
- Parameters
- evalsarray-like
Eigenvalues of a diffusion tensor.
- axisint
Axis of evals which contains 3 eigenvalues.
- Returns
- gaarray
Calculated GA. In the range 0 to +infinity
Notes
GA is calculated using the following equation given in [1]:
\[GA = \sqrt{\sum_{i=1}^3 \log^2{\left ( \lambda_i/<\mathbf{D}> \right )}}, \quad \textrm{where} \quad <\mathbf{D}> = (\lambda_1\lambda_2\lambda_3)^{1/3}\]Note that the notation, \(<D>\), is often used as the mean diffusivity (MD) of the diffusion tensor and can lead to confusions in the literature (see [1] versus [2] versus [3] for example). Reference [2] defines geodesic anisotropy (GA) with \(<D>\) as the MD in the denominator of the sum. This is wrong. The original paper [1] defines GA with \(<D> = det(D)^{1/3}\), as the isotropic part of the distance. This might be an explanation for the confusion. The isotropic part of the diffusion tensor in Euclidean space is the MD whereas the isotropic part of the tensor in log-Euclidean space is \(det(D)^{1/3}\). The Appendix of [1] and log-Euclidean derivations from [3] are clear on this. Hence, all that to say that \(<D> = det(D)^{1/3}\) here for the GA definition and not MD.
References
- 1(1,2,3,4,5)
P. G. Batchelor, M. Moakher, D. Atkinson, F. Calamante, A. Connelly, “A rigorous framework for diffusion tensor calculus”, Magnetic Resonance in Medicine, vol. 53, pp. 221-225, 2005.
- 2(1,2,3)
M. M. Correia, V. F. Newcombe, G.B. Williams. “Contrast-to-noise ratios for indices of anisotropy obtained from diffusion MRI: a study with standard clinical b-values at 3T”. NeuroImage, vol. 57, pp. 1103-1115, 2011.
- 3(1,2,3)
A. D. Lee, etal, P. M. Thompson. “Comparison of fractional and geodesic anisotropy in diffusion tensor images of 90 monozygotic and dizygotic twins”. 5th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 943-946, May 2008.
- 4
V. Arsigny, P. Fillard, X. Pennec, N. Ayache. “Log-Euclidean metrics for fast and simple calculus on diffusion tensors.” Magnetic Resonance in Medecine, vol 56, pp. 411-421, 2006.
get_mode¶
-
dipy.workflows.reconst.get_mode(q_form)¶ Mode (MO) of a diffusion tensor [1].
- Parameters
- q_formndarray
The quadratic form of a tensor, or an array with quadratic forms of tensors. Should be of shape (x, y, z, 3, 3) or (n, 3, 3) or (3, 3).
- Returns
- modearray
Calculated tensor mode in each spatial coordinate.
Notes
Mode ranges between -1 (planar anisotropy) and +1 (linear anisotropy) with 0 representing orthotropy. Mode is calculated with the following equation (equation 9 in [1]):
\[Mode = 3*\sqrt{6}*det(\widetilde{A}/norm(\widetilde{A}))\]Where \(\widetilde{A}\) is the deviatoric part of the tensor quadratic form.
References
gradient_table¶
-
dipy.workflows.reconst.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01)¶ A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.
- Parameters
- bvalscan be any of the four options
an array of shape (N,) or (1, N) or (N, 1) with the b-values.
a path for the file which contains an array like the above (1).
an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.
a path for the file which contains an array like the one at (3).
- bvecscan be any of two options
an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.
- big_deltafloat
acquisition pulse separation time in seconds (default None)
- small_deltafloat
acquisition pulse duration time in seconds (default None)
- b0_thresholdfloat
All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.
- atolfloat
All b-vectors need to be unit vectors up to a tolerance.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
Notes
Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.
Examples
>>> from dipy.core.gradients import gradient_table >>> bvals = 1500 * np.ones(7) >>> bvals[0] = 0 >>> sq2 = np.sqrt(2) / 2 >>> bvecs = np.array([[0, 0, 0], ... [1, 0, 0], ... [0, 1, 0], ... [0, 0, 1], ... [sq2, sq2, 0], ... [sq2, 0, sq2], ... [0, sq2, sq2]]) >>> gt = gradient_table(bvals, bvecs) >>> gt.bvecs.shape == bvecs.shape True >>> gt = gradient_table(bvals, bvecs.T) >>> gt.bvecs.shape == bvecs.T.shape False
literal_eval¶
-
dipy.workflows.reconst.literal_eval(node_or_string)¶ Safely evaluate an expression node or a string containing a Python expression. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.
load_nifti¶
-
dipy.workflows.reconst.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
lower_triangular¶
-
dipy.workflows.reconst.lower_triangular(tensor, b0=None)¶ Returns the six lower triangular values of the tensor and a dummy variable if b0 is not None
- Parameters
- tensorarray_like (…, 3, 3)
a collection of 3, 3 diffusion tensors
- b0float
if b0 is not none log(b0) is returned as the dummy variable
- Returns
- Dndarray
If b0 is none, then the shape will be (…, 6) otherwise (…, 7)
mean_diffusivity¶
-
dipy.workflows.reconst.mean_diffusivity(evals, axis=-1)¶ Mean Diffusivity (MD) of a diffusion tensor.
- Parameters
- evalsarray-like
Eigenvalues of a diffusion tensor.
- axisint
Axis of evals which contains 3 eigenvalues.
- Returns
- mdarray
Calculated MD.
Notes
MD is calculated with the following equation:
\[MD = \frac{\lambda_1 + \lambda_2 + \lambda_3}{3}\]
nifti1_symmat¶
-
dipy.workflows.reconst.nifti1_symmat(image_data, *args, **kwargs)¶ Returns a Nifti1Image with a symmetric matrix intent
- Parameters
- image_dataarray-like
should have lower triangular elements of a symmetric matrix along the last dimension
- all other arguments and keywords are passed to Nifti1Image
- Returns
- imageNifti1Image
5d, extra dimensions addes before the last. Has symmetric matrix intent code
peaks_from_model¶
-
dipy.workflows.reconst.peaks_from_model(model, data, sphere, relative_peak_threshold, min_separation_angle, mask=None, return_odf=False, return_sh=True, gfa_thr=0, normalize_peaks=False, sh_order=8, sh_basis_type=None, npeaks=5, B=None, invB=None, parallel=False, nbr_processes=None)¶ Fit the model to data and computes peaks and metrics
- Parameters
- modela model instance
model will be used to fit the data.
- sphereSphere
The Sphere providing discrete directions for evaluation.
- relative_peak_thresholdfloat
Only return peaks greater than
relative_peak_threshold * mwhere m is the largest peak.- min_separation_anglefloat in [0, 90] The minimum distance between
directions. If two peaks are too close only the larger of the two is returned.
- maskarray, optional
If mask is provided, voxels that are False in mask are skipped and no peaks are returned.
- return_odfbool
If True, the odfs are returned.
- return_shbool
If True, the odf as spherical harmonics coefficients is returned
- gfa_thrfloat
Voxels with gfa less than gfa_thr are skipped, no peaks are returned.
- normalize_peaksbool
If true, all peak values are calculated relative to max(odf).
- sh_orderint, optional
Maximum SH order in the SH fit. For sh_order, there will be
(sh_order + 1) * (sh_order + 2) / 2SH coefficients (default 8).- sh_basis_type{None, ‘tournier07’, ‘descoteaux07’}
Nonefor the default DIPY basis,tournier07for the Tournier 2007 [2] basis, anddescoteaux07for the Descoteaux 2007 [1] basis (Nonedefaults todescoteaux07).- sh_smoothfloat, optional
Lambda-regularization in the SH fit (default 0.0).
- npeaksint
Maximum number of peaks found (default 5 peaks).
- Bndarray, optional
Matrix that transforms spherical harmonics to spherical function
sf = np.dot(sh, B).- invBndarray, optional
Inverse of B.
- parallel: bool
If True, use multiprocessing to compute peaks and metric (default False). Temporary files are saved in the default temporary directory of the system. It can be changed using
import tempfileandtempfile.tempdir = '/path/to/tempdir'.- nbr_processes: int
If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()).
- Returns
- pamPeaksAndMetrics
An object with
gfa,peak_directions,peak_values,peak_indices,odf,shm_coeffsas attributes
References
- 1(1,2)
Descoteaux, M., Angelino, E., Fitzgibbons, S. and Deriche, R. Regularized, Fast, and Robust Analytical Q-ball Imaging. Magn. Reson. Med. 2007;58:497-510.
- 2(1,2)
Tournier J.D., Calamante F. and Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35(4):1459-1472.
peaks_to_niftis¶
-
dipy.workflows.reconst.peaks_to_niftis(pam, fname_shm, fname_dirs, fname_values, fname_indices, fname_gfa, reshape_dirs=False)¶ Save SH, directions, indices and values of peaks to Nifti.
radial_diffusivity¶
-
dipy.workflows.reconst.radial_diffusivity(evals, axis=-1)¶ Radial Diffusivity (RD) of a diffusion tensor. Also called perpendicular diffusivity.
- Parameters
- evalsarray-like
Eigenvalues of a diffusion tensor, must be sorted in descending order along axis.
- axisint
Axis of evals which contains 3 eigenvalues.
- Returns
- rdarray
Calculated RD.
Notes
RD is calculated with the following equation:
\[RD = \frac{\lambda_2 + \lambda_3}{2}\]
read_bvals_bvecs¶
-
dipy.workflows.reconst.read_bvals_bvecs(fbvals, fbvecs)¶ Read b-values and b-vectors from disk
- Parameters
- fbvalsstr
Full path to file with b-values. None to not read bvals.
- fbvecsstr
Full path of file with b-vectors. None to not read bvecs.
- Returns
- bvalsarray, (N,) or None
- bvecsarray, (N, 3) or None
Notes
Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).
save_peaks¶
-
dipy.workflows.reconst.save_peaks(fname, pam, affine=None, verbose=False)¶ Save all important attributes of object PeaksAndMetrics in a PAM5 file (HDF5).
- Parameters
- fnamestring
Filename of PAM5 file
- pamPeaksAndMetrics
Object holding peak_dirs, shm_coeffs and other attributes
- affinearray
The 4x4 matrix transforming the date from native to world coordinates. PeaksAndMetrics should have that attribute but if not it can be provided here. Default None.
- verbosebool
Print summary information about the saved file.
split_dki_param¶
-
dipy.workflows.reconst.split_dki_param(dki_params)¶ Extract the diffusion tensor eigenvalues, the diffusion tensor eigenvector matrix, and the 15 independent elements of the kurtosis tensor from the model parameters estimated from the DKI model
- Parameters
- dki_paramsndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model. Parameters are ordered as follows:
Three diffusion tensor’s eigenvalues
Three lines of the eigenvector matrix each containing the first, second and third coordinates of the eigenvector
Fifteen elements of the kurtosis tensor
- Returns
- eigvalsarray (x, y, z, 3) or (n, 3)
Eigenvalues from eigen decomposition of the tensor.
- eigvecsarray (x, y, z, 3, 3) or (n, 3, 3)
Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j])
- ktarray (x, y, z, 15) or (n, 15)
Fifteen elements of the kurtosis tensor
LabelsBundlesFlow¶
-
class
dipy.workflows.segment.LabelsBundlesFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(streamline_files, labels_files[, …])Extract bundles using existing indices (labels)
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(streamline_files, labels_files, out_dir='', out_bundle='recognized_orig.trk')¶ Extract bundles using existing indices (labels)
- Parameters
- streamline_filesstring
The path of streamline files where you want to recognize bundles
- labels_filesstring
The path of model bundle files
- out_dirstring, optional
Output directory (default input file directory)
- out_bundlestring, optional
Recognized bundle in the space of the model bundle (default ‘recognized_orig.trk’)
References
- Garyfallidis17
Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
-
MedianOtsuFlow¶
-
class
dipy.workflows.segment.MedianOtsuFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files[, save_masked, …])Workflow wrapping the median_otsu segmentation method.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, save_masked=False, median_radius=2, numpass=5, autocrop=False, vol_idx=None, dilate=None, out_dir='', out_mask='brain_mask.nii.gz', out_masked='dwi_masked.nii.gz')¶ Workflow wrapping the median_otsu segmentation method.
Applies median_otsu segmentation on each file found by ‘globing’
input_filesand saves the results in a directory specified byout_dir.- Parameters
- input_filesstring
Path to the input volumes. This path may contain wildcards to process multiple inputs at once.
- save_maskedbool, optional
Save mask
- median_radiusint, optional
Radius (in voxels) of the applied median filter (default 2)
- numpassint, optional
Number of pass of the median filter (default 5)
- autocropbool, optional
If True, the masked input_volumes will also be cropped using the bounding box defined by the masked data. For example, if diffusion images are of 1x1x1 (mm^3) or higher resolution auto-cropping could reduce their size in memory and speed up some of the analysis. (default False)
- vol_idxvariable int, optional
1D array representing indices of
axis=-1of a 4D input_volume. From the command line use something like 3 4 5 6. From script use something like [3, 4, 5, 6]. This input is required for 4D volumes.- dilateint, optional
number of iterations for binary dilation (default ‘None’)
- out_dirstring, optional
Output directory (default input file directory)
- out_maskstring, optional
Name of the mask volume to be saved (default ‘brain_mask.nii.gz’)
- out_maskedstring, optional
Name of the masked volume to be saved (default ‘dwi_masked.nii.gz’)
-
RecoBundles¶
-
class
dipy.workflows.segment.RecoBundles(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=True)¶ Bases:
objectMethods
evaluate_results(model_bundle, …)Compare the similiarity between two given bundles, model bundle, and extracted bundle.
recognize(model_bundle, model_clust_thr[, …])Recognize the model_bundle in self.streamlines
refine(model_bundle, pruned_streamlines, …)Refine and recognize the model_bundle in self.streamlines This method expects once pruned streamlines as input.
-
__init__(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=True)¶ Recognition of bundles
Extract bundles from a participants’ tractograms using model bundles segmented from a different subject or an atlas of bundles. See [Garyfallidis17] for the details.
- Parameters
- streamlinesStreamlines
The tractogram in which you want to recognize bundles.
- greater_thanint, optional
Keep streamlines that have length greater than this value (default 50)
- less_thanint, optional
Keep streamlines have length less than this value (default 1000000)
- cluster_mapQB map
Provide existing clustering to start RB faster (default None).
- clust_thrfloat
Distance threshold in mm for clustering streamlines
- rngRandomState
If None define RandomState in initialization function.
- nb_ptsint
Number of points per streamline (default 20)
Notes
Make sure that before creating this class that the streamlines and the model bundles are roughly in the same space. Also default thresholds are assumed in RAS 1mm^3 space. You may want to adjust those if your streamlines are not in world coordinates.
References
-
evaluate_results(model_bundle, pruned_streamlines, slr_select)¶ Compare the similiarity between two given bundles, model bundle, and extracted bundle.
- Parameters
- model_bundleStreamlines
- pruned_streamlinesStreamlines
- slr_selecttuple
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
- Returns
- ba_valuefloat
bundle adjacency value between model bundle and pruned bundle
- bmd_valuefloat
bundle minimum distance value between model bundle and pruned bundle
-
recognize(model_bundle, model_clust_thr, reduction_thr=10, reduction_distance='mdf', slr=True, slr_num_threads=None, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=5, pruning_distance='mdf')¶ Recognize the model_bundle in self.streamlines
- Parameters
- model_bundleStreamlines
- model_clust_thrfloat
- reduction_thrfloat
- reduction_distancestring
mdf or mam (default mam)
- slrbool
Use Streamline-based Linear Registration (SLR) locally (default True)
- slr_metricBundleMinDistanceMetric
- slr_x0array
(default None)
- slr_boundsarray
(default None)
- slr_selecttuple
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
- slr_methodstring
Optimization method (default ‘L-BFGS-B’)
- pruning_thrfloat
- pruning_distancestring
MDF (‘mdf’) and MAM (‘mam’)
- Returns
- recognized_transfStreamlines
Recognized bundle in the space of the model tractogram
- recognized_labelsarray
Indices of recognized bundle in the original tractogram
References
- Garyfallidis17
Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
-
refine(model_bundle, pruned_streamlines, model_clust_thr, reduction_thr=14, reduction_distance='mdf', slr=True, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=6, pruning_distance='mdf')¶ Refine and recognize the model_bundle in self.streamlines This method expects once pruned streamlines as input. It refines the first ouput of recobundle by applying second local slr (optional), and second pruning. This method is useful when we are dealing with noisy data or when we want to extract small tracks from tractograms.
- Parameters
- model_bundleStreamlines
- pruned_streamlinesStreamlines
- model_clust_thrfloat
- reduction_thrfloat
- reduction_distancestring
mdf or mam (default mam)
- slrbool
Use Streamline-based Linear Registration (SLR) locally (default True)
- slr_metricBundleMinDistanceMetric
- slr_x0array
(default None)
- slr_boundsarray
(default None)
- slr_selecttuple
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
- slr_methodstring
Optimization method (default ‘L-BFGS-B’)
- pruning_thrfloat
- pruning_distancestring
MDF (‘mdf’) and MAM (‘mam’)
- Returns
- recognized_transfStreamlines
Recognized bundle in the space of the model tractogram
- recognized_labelsarray
Indices of recognized bundle in the original tractogram
References
- Garyfallidis17
Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
-
RecoBundlesFlow¶
-
class
dipy.workflows.segment.RecoBundlesFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(streamline_files, model_bundle_files[, …])Recognize bundles
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(streamline_files, model_bundle_files, greater_than=50, less_than=1000000, no_slr=False, clust_thr=15.0, reduction_thr=15.0, reduction_distance='mdf', model_clust_thr=2.5, pruning_thr=8.0, pruning_distance='mdf', slr_metric='symmetric', slr_transform='similarity', slr_matrix='small', refine=False, r_reduction_thr=12.0, r_pruning_thr=6.0, no_r_slr=False, out_dir='', out_recognized_transf='recognized.trk', out_recognized_labels='labels.npy')¶ Recognize bundles
- Parameters
- streamline_filesstring
The path of streamline files where you want to recognize bundles
- model_bundle_filesstring
The path of model bundle files
- greater_thanint, optional
Keep streamlines that have length greater than this value (default 50) in mm.
- less_thanint, optional
Keep streamlines have length less than this value (default 1000000) in mm.
- no_slrbool, optional
Don’t enable local Streamline-based Linear Registration (default False).
- clust_thrfloat, optional
MDF distance threshold for all streamlines (default 15)
- reduction_thrfloat, optional
Reduce search space by (mm) (default 15)
- reduction_distancestring, optional
Reduction distance type can be mdf or mam (default mdf)
- model_clust_thrfloat, optional
MDF distance threshold for the model bundles (default 2.5)
- pruning_thrfloat, optional
Pruning after matching (default 8).
- pruning_distancestring, optional
Pruning distance type can be mdf or mam (default mdf)
- slr_metricstring, optional
Options are None, symmetric, asymmetric or diagonal (default symmetric).
- slr_transformstring, optional
Transformation allowed. translation, rigid, similarity or scaling (Default ‘similarity’).
- slr_matrixstring, optional
Options are ‘nano’, ‘tiny’, ‘small’, ‘medium’, ‘large’, ‘huge’ (default ‘small’)
- refinebool, optional
Enable refine recognized bunle (default False)
- r_reduction_thrfloat, optional
Refine reduce search space by (mm) (default 12)
- r_pruning_thrfloat, optional
Refine pruning after matching (default 6).
- no_r_slrbool, optional
Don’t enable Refine local Streamline-based Linear Registration (default False).
- out_dirstring, optional
Output directory (default input file directory)
- out_recognized_transfstring, optional
Recognized bundle in the space of the model bundle (default ‘recognized.trk’)
- out_recognized_labelsstring, optional
Indices of recognized bundle in the original tractogram (default ‘labels.npy’)
References
- Garyfallidis17
Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
-
Workflow¶
-
class
dipy.workflows.segment.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
load_nifti¶
-
dipy.workflows.segment.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
median_otsu¶
-
dipy.workflows.segment.median_otsu(input_volume, vol_idx=None, median_radius=4, numpass=4, autocrop=False, dilate=None)¶ Simple brain extraction tool method for images from DWI data.
It uses a median filter smoothing of the input_volumes vol_idx and an automatic histogram Otsu thresholding technique, hence the name median_otsu.
This function is inspired from Mrtrix’s bet which has default values
median_radius=3,numpass=2. However, from tests on multiple 1.5T and 3T data from GE, Philips, Siemens, the most robust choice ismedian_radius=4,numpass=4.- Parameters
- input_volumendarray
3D or 4D array of the brain volume.
- vol_idxNone or array, optional.
1D array representing indices of
axis=3of a 4D input_volume. None is only an acceptable input ifinput_volumeis 3D.- median_radiusint
Radius (in voxels) of the applied median filter (default: 4).
- numpass: int
Number of pass of the median filter (default: 4).
- autocrop: bool, optional
if True, the masked input_volume will also be cropped using the bounding box defined by the masked data. Should be on if DWI is upsampled to 1x1x1 resolution. (default: False).
- dilateNone or int, optional
number of iterations for binary dilation
- Returns
- maskedvolumendarray
Masked input_volume
- mask3D ndarray
The binary brain mask
Notes
Copyright (C) 2011, the scikit-image team All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
time¶
-
dipy.workflows.segment.time() → floating point number¶ Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them.
BundleAnalysisPopulationFlow¶
-
class
dipy.workflows.stats.BundleAnalysisPopulationFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(model_bundle_folder, subject_folder[, …])Workflow of bundle analytics.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(model_bundle_folder, subject_folder, no_disks=100, out_dir='')¶ Workflow of bundle analytics.
Applies statistical analysis on bundles of subjects and saves the results in a directory specified by
out_dir.- Parameters
- model_bundle_folderstring
Path to the input model bundle files. This path may contain wildcards to process multiple inputs at once.
- subject_folderstring
Path to the input subject folder. This path may contain wildcards to process multiple inputs at once.
- no_disksinteger, optional
Number of disks used for dividing bundle into disks. (Default 100)
- out_dirstring, optional
Output directory (default input file directory)
References
- Chandio19
Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical analyses framework for tractometric studies, Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
-
LinearMixedModelsFlow¶
-
class
dipy.workflows.stats.LinearMixedModelsFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(h5_files[, no_disks, out_dir])Workflow of linear Mixed Models.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(h5_files, no_disks=100, out_dir='')¶ Workflow of linear Mixed Models.
Applies linear Mixed Models on bundles of subjects and saves the results in a directory specified by
out_dir.- Parameters
- h5_filesstring
Path to the input metric files. This path may contain wildcards to process multiple inputs at once.
- no_disksinteger, optional
Number of disks used for dividing bundle into disks. (Default 100)
- out_dirstring, optional
Output directory (default input file directory)
-
SNRinCCFlow¶
-
class
dipy.workflows.stats.SNRinCCFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(data_files, bvals_files, bvecs_files, …)Compute the signal-to-noise ratio in the corpus callosum.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(data_files, bvals_files, bvecs_files, mask_file, bbox_threshold=[0.6, 1, 0, 0.1, 0, 0.1], out_dir='', out_file='product.json', out_mask_cc='cc.nii.gz', out_mask_noise='mask_noise.nii.gz')¶ Compute the signal-to-noise ratio in the corpus callosum.
- Parameters
- data_filesstring
Path to the dwi.nii.gz file. This path may contain wildcards to process multiple inputs at once.
- bvals_filesstring
Path of bvals.
- bvecs_filesstring
Path of bvecs.
- mask_filestring
Path of a brain mask file.
- bbox_thresholdvariable float, optional
Threshold for bounding box, values separated with commas for ex. [0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
- out_dirstring, optional
Where the resulting file will be saved. (default ‘’)
- out_filestring, optional
Name of the result file to be saved. (default ‘product.json’)
- out_mask_ccstring, optional
Name of the CC mask volume to be saved (default ‘cc.nii.gz’)
- out_mask_noisestring, optional
Name of the mask noise volume to be saved (default ‘mask_noise.nii.gz’)
-
TensorModel¶
-
class
dipy.workflows.stats.TensorModel(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)¶ Bases:
dipy.reconst.base.ReconstModelDiffusion Tensor
Methods
fit(data[, mask])Fit method of the DTI model class
predict(dti_params[, S0])Predict a signal for this TensorModel class instance given parameters.
-
__init__(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)¶ A Diffusion Tensor Model [1], [2].
- Parameters
- gtabGradientTable class instance
- fit_methodstr or callable
str can be one of the following:
- ‘WLS’ for weighted least squares
dti.wls_fit_tensor()- ‘LS’ or ‘OLS’ for ordinary least squares
dti.ols_fit_tensor()- ‘NLLS’ for non-linear least-squares
dti.nlls_fit_tensor()- ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor
fitting [3]
dti.restore_fit_tensor()- callable has to have the signature:
- return_S0_hatbool
Boolean to return (True) or not (False) the S0 values for the fit.
- args, kwargsarguments and key-word arguments passed to the
fit_method. See dti.wls_fit_tensor, dti.ols_fit_tensor for details
- min_signalfloat
The minimum signal value. Needs to be a strictly positive number. Default: minimal signal in the data provided to fit.
Notes
In order to increase speed of processing, tensor fitting is done simultaneously over many voxels. Many fit_methods use the ‘step’ parameter to set the number of voxels that will be fit at once in each iteration. This is the chunk size as a number of voxels. A larger step value should speed things up, but it will also take up more memory. It is advisable to keep an eye on memory consumption as this value is increased.
E.g., in
iter_fit_tensor()we have a default step value of 1e4References
- 1(1,2)
Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254.
- 2(1,2)
Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
- 3(1,2)
Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095
-
fit(data, mask=None)¶ Fit method of the DTI model class
- Parameters
- dataarray
The measured signal from one voxel.
- maskarray
A boolean array used to mark the coordinates in the data that should be analyzed that has the shape data.shape[:-1]
-
predict(dti_params, S0=1.0)¶ Predict a signal for this TensorModel class instance given parameters.
- Parameters
- dti_paramsndarray
The last dimension should have 12 tensor parameters: 3 eigenvalues, followed by the 3 eigenvectors
- S0float or ndarray
The non diffusion-weighted signal in every voxel, or across all voxels. Default: 1
-
Workflow¶
-
class
dipy.workflows.stats.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
binary_dilation¶
-
dipy.workflows.stats.binary_dilation(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False)¶ Multi-dimensional binary dilation with the given structuring element.
- Parameters
- inputarray_like
Binary array_like to be dilated. Non-zero (True) elements form the subset to be dilated.
- structurearray_like, optional
Structuring element used for the dilation. Non-zero elements are considered True. If no structuring element is provided an element is generated with a square connectivity equal to one.
- iterations{int, float}, optional
The dilation is repeated iterations times (one, by default). If iterations is less than 1, the dilation is repeated until the result does not change anymore.
- maskarray_like, optional
If a mask is given, only those elements with a True value at the corresponding mask element are modified at each iteration.
- outputndarray, optional
Array of the same shape as input, into which the output is placed. By default, a new array is created.
- border_valueint (cast to 0 or 1), optional
Value at the border in the output array.
- originint or tuple of ints, optional
Placement of the filter, by default 0.
- brute_forceboolean, optional
Memory condition: if False, only the pixels whose value was changed in the last iteration are tracked as candidates to be updated (dilated) in the current iteration; if True all pixels are considered as candidates for dilation, regardless of what happened in the previous iteration. False by default.
- Returns
- binary_dilationndarray of bools
Dilation of the input by the structuring element.
See also
grey_dilation,binary_erosion,binary_closing,binary_opening,generate_binary_structureNotes
Dilation [1] is a mathematical morphology operation [2] that uses a structuring element for expanding the shapes in an image. The binary dilation of an image by a structuring element is the locus of the points covered by the structuring element, when its center lies within the non-zero points of the image.
References
Examples
>>> from scipy import ndimage >>> a = np.zeros((5, 5)) >>> a[2, 2] = 1 >>> a array([[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a) array([[False, False, False, False, False], [False, False, True, False, False], [False, True, True, True, False], [False, False, True, False, False], [False, False, False, False, False]], dtype=bool) >>> ndimage.binary_dilation(a).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> # 3x3 structuring element with connectivity 1, used by default >>> struct1 = ndimage.generate_binary_structure(2, 1) >>> struct1 array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> # 3x3 structuring element with connectivity 2 >>> struct2 = ndimage.generate_binary_structure(2, 2) >>> struct2 array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) >>> ndimage.binary_dilation(a, structure=struct1).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a, structure=struct2).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 1., 1., 1., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a, structure=struct1,\ ... iterations=2).astype(a.dtype) array([[ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 1., 1., 1., 1., 1.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.]])
bounding_box¶
-
dipy.workflows.stats.bounding_box(vol)¶ Compute the bounding box of nonzero intensity voxels in the volume.
- Parameters
- volndarray
Volume to compute bounding box on.
- Returns
- npminslist
Array containg minimum index of each dimension
- npmaxslist
Array containg maximum index of each dimension
bundle_analysis¶
-
dipy.workflows.stats.bundle_analysis(model_bundle_folder, bundle_folder, orig_bundle_folder, metric_folder, group, subject, no_disks=100, out_dir='')¶ Applies statistical analysis on bundles and saves the results in a directory specified by
out_dir.- Parameters
- model_bundle_folderstring
Path to the input model bundle files. This path may contain wildcards to process multiple inputs at once.
- bundle_folderstring
Path to the input bundle files in common space. This path may contain wildcards to process multiple inputs at once.
- orig_folderstring
Path to the input bundle files in native space. This path may contain wildcards to process multiple inputs at once.
- metric_folderstring
Path to the input dti metric or/and peak files. It will be used as metric for statistical analysis of bundles.
- groupstring
what group subject belongs to e.g. control or patient
- subjectstring
subject id e.g. 10001
- no_disksinteger, optional
Number of disks used for dividing bundle into disks. (Default 100)
- out_dirstring, optional
Output directory (default input file directory)
References
- Chandio19
Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical analyses framework for tractometric studies, Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
gradient_table¶
-
dipy.workflows.stats.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01)¶ A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.
- Parameters
- bvalscan be any of the four options
an array of shape (N,) or (1, N) or (N, 1) with the b-values.
a path for the file which contains an array like the above (1).
an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.
a path for the file which contains an array like the one at (3).
- bvecscan be any of two options
an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.
- big_deltafloat
acquisition pulse separation time in seconds (default None)
- small_deltafloat
acquisition pulse duration time in seconds (default None)
- b0_thresholdfloat
All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.
- atolfloat
All b-vectors need to be unit vectors up to a tolerance.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
Notes
Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.
Examples
>>> from dipy.core.gradients import gradient_table >>> bvals = 1500 * np.ones(7) >>> bvals[0] = 0 >>> sq2 = np.sqrt(2) / 2 >>> bvecs = np.array([[0, 0, 0], ... [1, 0, 0], ... [0, 1, 0], ... [0, 0, 1], ... [sq2, sq2, 0], ... [sq2, 0, sq2], ... [0, sq2, sq2]]) >>> gt = gradient_table(bvals, bvecs) >>> gt.bvecs.shape == bvecs.shape True >>> gt = gradient_table(bvals, bvecs.T) >>> gt.bvecs.shape == bvecs.T.shape False
load_nifti¶
-
dipy.workflows.stats.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
median_otsu¶
-
dipy.workflows.stats.median_otsu(input_volume, vol_idx=None, median_radius=4, numpass=4, autocrop=False, dilate=None)¶ Simple brain extraction tool method for images from DWI data.
It uses a median filter smoothing of the input_volumes vol_idx and an automatic histogram Otsu thresholding technique, hence the name median_otsu.
This function is inspired from Mrtrix’s bet which has default values
median_radius=3,numpass=2. However, from tests on multiple 1.5T and 3T data from GE, Philips, Siemens, the most robust choice ismedian_radius=4,numpass=4.- Parameters
- input_volumendarray
3D or 4D array of the brain volume.
- vol_idxNone or array, optional.
1D array representing indices of
axis=3of a 4D input_volume. None is only an acceptable input ifinput_volumeis 3D.- median_radiusint
Radius (in voxels) of the applied median filter (default: 4).
- numpass: int
Number of pass of the median filter (default: 4).
- autocrop: bool, optional
if True, the masked input_volume will also be cropped using the bounding box defined by the masked data. Should be on if DWI is upsampled to 1x1x1 resolution. (default: False).
- dilateNone or int, optional
number of iterations for binary dilation
- Returns
- maskedvolumendarray
Masked input_volume
- mask3D ndarray
The binary brain mask
Notes
Copyright (C) 2011, the scikit-image team All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
optional_package¶
-
dipy.workflows.stats.optional_package(name, trip_msg=None)¶ Return package-like thing and module setup for package name
- Parameters
- namestr
package name
- trip_msgNone or str
message to give when someone tries to use the return package, but we could not import it, and have returned a TripWire object instead. Default message if None.
- Returns
- pkg_likemodule or
TripWireinstance If we can import the package, return it. Otherwise return an object raising an error when accessed
- have_pkgbool
True if import for package was successful, false otherwise
- module_setupfunction
callable usually set as
setup_modulein calling namespace, to allow skipping tests.
- pkg_likemodule or
Examples
Typical use would be something like this at the top of a module using an optional package:
>>> from dipy.utils.optpkg import optional_package >>> pkg, have_pkg, setup_module = optional_package('not_a_package')
Of course in this case the package doesn’t exist, and so, in the module:
>>> have_pkg False
and
>>> pkg.some_function() #doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TripWireError: We need package not_a_package for these functions, but ``import not_a_package`` raised an ImportError
If the module does exist - we get the module
>>> pkg, _, _ = optional_package('os') >>> hasattr(pkg, 'path') True
Or a submodule if that’s what we asked for
>>> subpkg, _, _ = optional_package('os.path') >>> hasattr(subpkg, 'dirname') True
read_bvals_bvecs¶
-
dipy.workflows.stats.read_bvals_bvecs(fbvals, fbvecs)¶ Read b-values and b-vectors from disk
- Parameters
- fbvalsstr
Full path to file with b-values. None to not read bvals.
- fbvecsstr
Full path of file with b-vectors. None to not read bvecs.
- Returns
- bvalsarray, (N,) or None
- bvecsarray, (N, 3) or None
Notes
Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).
segment_from_cfa¶
-
dipy.workflows.stats.segment_from_cfa(tensor_fit, roi, threshold, return_cfa=False)¶ Segment the cfa inside roi using the values from threshold as bounds.
- Parameters
- tensor_fitTensorFit object
TensorFit object
- roindarray
A binary mask, which contains the bounding box for the segmentation.
- thresholdarray-like
An iterable that defines the min and max values to use for the thresholding. The values are specified as (R_min, R_max, G_min, G_max, B_min, B_max)
- return_cfabool, optional
If True, the cfa is also returned.
- Returns
- maskndarray
Binary mask of the segmentation.
- cfandarray, optional
Array with shape = (…, 3), where … is the shape of tensor_fit. The color fractional anisotropy, ordered as a nd array with the last dimension of size 3 for the R, G and B channels.
simple_plot¶
-
dipy.workflows.stats.simple_plot(file_name, title, x, y, xlabel, ylabel)¶ Saves the simple plot with given x and y values
- Parameters
- file_namestring
file name for saving the plot
- titlestring
title of the plot
- xinteger list
x-axis values to be ploted
- yinteger list
y-axis values to be ploted
- xlabelstring
label for x-axis
- ylablestring
label for y-axis
ClosestPeakDirectionGetter¶
-
class
dipy.workflows.tracking.ClosestPeakDirectionGetter¶ Bases:
dipy.direction.closest_peak_direction_getter.PmfGenDirectionGetterA direction getter that returns the closest odf peak to previous tracking direction.
Methods
from_pmfConstructor for making a DirectionGetter from an array of Pmfs
from_shcoeffProbabilistic direction getter from a distribution of directions on the sphere
initial_directionReturns best directions at seed location to start tracking.
get_direction
-
__init__($self, /, *args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
-
CmcStoppingCriterion¶
-
class
dipy.workflows.tracking.CmcStoppingCriterion¶ Bases:
dipy.tracking.stopping_criterion.AnatomicalStoppingCriterionContinuous map criterion (CMC) stopping criterion from [1]. This implements the use of partial volume fraction (PVE) maps to determine when the tracking stops.
- cdef:
double interp_out_double[1] double[:] interp_out_view = interp_out_view double[:, :, :] include_map, exclude_map double step_size double average_voxel_size double correction_factor
References
“Towards quantitative connectivity analysis: reducing tractography biases.” NeuroImage, 98, 266-278, 2014.
Methods
from_pveAnatomicalStoppingCriterion from partial volume fraction (PVE) maps.
check_point
get_exclude
get_include
-
__init__($self, /, *args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
DeterministicMaximumDirectionGetter¶
-
class
dipy.workflows.tracking.DeterministicMaximumDirectionGetter¶ Bases:
dipy.direction.probabilistic_direction_getter.ProbabilisticDirectionGetterReturn direction of a sphere with the highest probability mass function (pmf).
Methods
from_pmfConstructor for making a DirectionGetter from an array of Pmfs
from_shcoeffProbabilistic direction getter from a distribution of directions on the sphere
initial_directionReturns best directions at seed location to start tracking.
get_direction
-
__init__($self, /, *args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
-
LocalFiberTrackingPAMFlow¶
-
class
dipy.workflows.tracking.LocalFiberTrackingPAMFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(pam_files, stopping_files, seeding_files)Workflow for Local Fiber Tracking.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(pam_files, stopping_files, seeding_files, use_binary_mask=False, stopping_thr=0.2, seed_density=1, step_size=0.5, tracking_method='eudx', pmf_threshold=0.1, max_angle=30.0, out_dir='', out_tractogram='tractogram.trk', save_seeds=False)¶ Workflow for Local Fiber Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
- Parameters
- pam_filesstring
- Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
- stopping_filesstring
Path to images (e.g. FA) used for stopping criterion for tracking.
- seeding_filesstring
A binary image showing where we need to seed for tracking.
- use_binary_maskbool, optional
If True, uses a binary stopping criterion. If the provided stopping_files are not binary, stopping_thr will be used to binarize the images.
- stopping_thrfloat, optional
Threshold applied to stopping volume’s data to identify where tracking has to stop (default 0.2).
- seed_densityint, optional
- Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed points in the voxel. And seed density of 1 means 1 point at the center of the voxel.
- step_sizefloat, optional
Step size used for tracking (default 0.5mm).
- tracking_methodstring, optional
- Select direction getter strategy :
“eudx” (Uses the peaks saved in the pam_files)
“deterministic” or “det” for a deterministic tracking (Uses the sh saved in the pam_files, default)
“probabilistic” or “prob” for a Probabilistic tracking (Uses the sh saved in the pam_files)
“closestpeaks” or “cp” for a ClosestPeaks tracking (Uses the sh saved in the pam_files)
- pmf_thresholdfloat, optional
Threshold for ODF functions (default 0.1).
- max_anglefloat, optional
Maximum angle between streamline segments (range [0, 90], default 30).
- out_dirstring, optional
Output directory (default input file directory).
- out_tractogramstring, optional
Name of the tractogram file to be saved (default ‘tractogram.trk’).
- save_seedsbool, optional
If true, save the seeds associated to their streamline in the ‘data_per_streamline’ Tractogram dictionary using ‘seeds’ as the key.
References
Garyfallidis, University of Cambridge, PhD thesis 2012. Amirbekian, University of California San Francisco, PhD thesis 2017.
-
LocalTracking¶
-
class
dipy.workflows.tracking.LocalTracking(direction_getter, stopping_criterion, seeds, affine, step_size, max_cross=None, maxlen=500, fixedstep=True, return_all=True, random_seed=None, save_seeds=False)¶ Bases:
object-
__init__(direction_getter, stopping_criterion, seeds, affine, step_size, max_cross=None, maxlen=500, fixedstep=True, return_all=True, random_seed=None, save_seeds=False)¶ Creates streamlines by using local fiber-tracking.
- Parameters
- direction_getterinstance of DirectionGetter
Used to get directions for fiber tracking.
- stopping_criterioninstance of StoppingCriterion
Identifies endpoints and invalid points to inform tracking.
- seedsarray (N, 3)
Points to seed the tracking. Seed points should be given in point space of the track (see
affine).- affinearray (4, 4)
Coordinate space for the streamline point with respect to voxel indices of input data. This affine can contain scaling, rotational, and translational components but should not contain any shearing. An identity matrix can be used to generate streamlines in “voxel coordinates” as long as isotropic voxels were used to acquire the data.
- step_sizefloat
Step size used for tracking.
- max_crossint or None
The maximum number of direction to track from each seed in crossing voxels. By default all initial directions are tracked.
- maxlenint
Maximum number of steps to track from seed. Used to prevent infinite loops.
- fixedstepbool
If true, a fixed stepsize is used, otherwise a variable step size is used.
- return_allbool
If true, return all generated streamlines, otherwise only streamlines reaching end points or exiting the image.
- random_seedint
The seed for the random seed generator (numpy.random.seed and random.seed).
- save_seedsbool
If True, return seeds alongside streamlines
-
PFTrackingPAMFlow¶
-
class
dipy.workflows.tracking.PFTrackingPAMFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(pam_files, wm_files, gm_files, …[, …])Workflow for Particle Filtering Tracking.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(pam_files, wm_files, gm_files, csf_files, seeding_files, step_size=0.2, seed_density=1, pmf_threshold=0.1, max_angle=20.0, pft_back=2, pft_front=1, pft_count=15, out_dir='', out_tractogram='tractogram.trk', save_seeds=False)¶ Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
- Parameters
- pam_filesstring
- Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
- wm_filesstring
Path to white matter partial volume estimate for tracking (CMC).
- gm_filesstring
Path to grey matter partial volume estimate for tracking (CMC).
- csf_filesstring
Path to cerebrospinal fluid partial volume estimate for tracking (CMC).
- seeding_filesstring
A binary image showing where we need to seed for tracking.
- step_sizefloat, optional
Step size used for tracking (default 0.2mm).
- seed_densityint, optional
- Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed points in the voxel. And seed density of 1 means 1 point at the center of the voxel.
- pmf_thresholdfloat, optional
Threshold for ODF functions (default 0.1).
- max_anglefloat, optional
Maximum angle between streamline segments (range [0, 90], default 20).
- pft_backfloat, optional
Distance in mm to back track before starting the particle filtering tractography (default 2mm). The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.
- pft_frontfloat, optional
Distance in mm to run the particle filtering tractography after the the back track distance (default 1mm). The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.
- pft_countint, optional
Number of particles to use in the particle filter (default 15).
- out_dirstring, optional
Output directory (default input file directory)
- out_tractogramstring, optional
Name of the tractogram file to be saved (default ‘tractogram.trk’)
- save_seedsbool, optional
If true, save the seeds associated to their streamline in the ‘data_per_streamline’ Tractogram dictionary using ‘seeds’ as the key
References
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage, 98, 266-278, 2014.
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ParticleFilteringTracking¶
-
class
dipy.workflows.tracking.ParticleFilteringTracking(direction_getter, stopping_criterion, seeds, affine, step_size, max_cross=None, maxlen=500, pft_back_tracking_dist=2, pft_front_tracking_dist=1, pft_max_trial=20, particle_count=15, return_all=True, random_seed=None, save_seeds=False)¶ Bases:
dipy.tracking.local_tracking.LocalTracking-
__init__(direction_getter, stopping_criterion, seeds, affine, step_size, max_cross=None, maxlen=500, pft_back_tracking_dist=2, pft_front_tracking_dist=1, pft_max_trial=20, particle_count=15, return_all=True, random_seed=None, save_seeds=False)¶ A streamline generator using the particle filtering tractography method [1].
- Parameters
- direction_getterinstance of ProbabilisticDirectionGetter
Used to get directions for fiber tracking.
- stopping_criterioninstance of AnatomicalStoppingCriterion
Identifies endpoints and invalid points to inform tracking.
- seedsarray (N, 3)
Points to seed the tracking. Seed points should be given in point space of the track (see
affine).- affinearray (4, 4)
Coordinate space for the streamline point with respect to voxel indices of input data. This affine can contain scaling, rotational, and translational components but should not contain any shearing. An identity matrix can be used to generate streamlines in “voxel coordinates” as long as isotropic voxels were used to acquire the data.
- step_sizefloat
Step size used for tracking.
- max_crossint or None
The maximum number of direction to track from each seed in crossing voxels. By default all initial directions are tracked.
- maxlenint
Maximum number of steps to track from seed. Used to prevent infinite loops.
- pft_back_tracking_distfloat
Distance in mm to back track before starting the particle filtering tractography. The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist. By default this is set to 2 mm.
- pft_front_tracking_distfloat
Distance in mm to run the particle filtering tractography after the the back track distance. The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist. By default this is set to 1 mm.
- pft_max_trialint
Maximum number of trial for the particle filtering tractography (Prevents infinite loops).
- particle_countint
Number of particles to use in the particle filter.
- return_allbool
If true, return all generated streamlines, otherwise only streamlines reaching end points or exiting the image.
- random_seedint
The seed for the random seed generator (numpy.random.seed and random.seed).
- save_seedsbool
If True, return seeds alongside streamlines
References
-
ProbabilisticDirectionGetter¶
-
class
dipy.workflows.tracking.ProbabilisticDirectionGetter¶ Bases:
dipy.direction.closest_peak_direction_getter.PmfGenDirectionGetterRandomly samples direction of a sphere based on probability mass function (pmf).
The main constructors for this class are current from_pmf and from_shcoeff. The pmf gives the probability that each direction on the sphere should be chosen as the next direction. To get the true pmf from the “raw pmf” directions more than
max_angledegrees from the incoming direction are set to 0 and the result is normalized.Methods
from_pmfConstructor for making a DirectionGetter from an array of Pmfs
from_shcoeffProbabilistic direction getter from a distribution of directions on the sphere
initial_directionReturns best directions at seed location to start tracking.
get_direction
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__init__()¶ Direction getter from a pmf generator.
- Parameters
- pmf_genPmfGen
Used to get probability mass function for selecting tracking directions.
- max_anglefloat, [0, 90]
The maximum allowed angle between incoming direction and new direction.
- sphereSphere
The set of directions to be used for tracking.
- pmf_thresholdfloat [0., 1.]
Used to remove direction from the probability mass function for selecting the tracking direction.
- relative_peak_thresholdfloat in [0., 1.]
Used for extracting initial tracking directions. Passed to peak_directions.
- min_separation_anglefloat in [0, 90]
Used for extracting initial tracking directions. Passed to peak_directions.
See also
-
StatefulTractogram¶
-
class
dipy.workflows.tracking.StatefulTractogram(streamlines, reference, space, shifted_origin=False, data_per_point=None, data_per_streamline=None)¶ Bases:
objectClass for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). Facilitate transformation between space and data manipulation for each streamline / point.
- Attributes
data_per_pointGetter for data_per_point
data_per_streamlineGetter for data_per_streamline
shifted_originGetter for shift
spaceGetter for the current space
space_attributeGetter for spatial attribute
streamlinesPartially safe getter for streamlines
Methods
Compute the bounding box of the streamlines in their current state
Safe getter for streamlines (for slicing)
Verify that the bounding box is valid in voxel space Will transform the streamlines for OBB, slow for big tractogram
Remove streamlines with invalid coordinates from the object.
Safe function to shift streamlines so the center of voxel is the origin
Safe function to shift streamlines so the corner of voxel is the origin
to_rasmm()Safe function to transform streamlines and update state
to_vox()Safe function to transform streamlines and update state
to_voxmm()Safe function to transform streamlines and update state
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__init__(streamlines, reference, space, shifted_origin=False, data_per_point=None, data_per_streamline=None)¶ Create a strict, state-aware, robust tractogram
- Parameters
- streamlineslist or ArraySequence
Streamlines of the tractogram
- referenceNifti or Trk filename, Nifti1Image or TrkFile,
Nifti1Header, trk.header (dict) or another Stateful Tractogram Reference that provides the spatial attribute. Typically a nifti-related object from the native diffusion used for streamlines generation
- spacestring
Current space in which the streamlines are (vox, voxmm or rasmm) Typically after tracking the space is VOX, after nibabel loading the space is RASMM
- shifted_originbool
Information on the position of the origin, False is Trackvis standard, default (corner of the voxel) True is NIFTI standard (center of the voxel)
- data_per_pointdict
Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i
- data_per_streamlinedict
Dictionary in which each key has X items X being the number of streamlines
Notes
Very important to respect the convention, verify that streamlines match the reference and are effectively in the right space.
Any change to the number of streamlines, data_per_point or data_per_streamline requires particular verification.
In a case of manipulation not allowed by this object, use Nibabel directly and be careful.
-
compute_bounding_box()¶ Compute the bounding box of the streamlines in their current state
- Returns
- outputndarray
8 corners of the XYZ aligned box, all zeros if no streamlines
-
property
data_per_point¶ Getter for data_per_point
-
property
data_per_streamline¶ Getter for data_per_streamline
-
get_streamlines_copy()¶ Safe getter for streamlines (for slicing)
-
is_bbox_in_vox_valid()¶ Verify that the bounding box is valid in voxel space Will transform the streamlines for OBB, slow for big tractogram
- Returns
- outputbool
Are the streamlines within the volume of the associated reference
-
remove_invalid_streamlines()¶ Remove streamlines with invalid coordinates from the object. Will also remove the data_per_point and data_per_streamline. Invalid coordinates are any X,Y,Z values above the reference dimensions or below zero Returns ——- output : tuple
Tuple of two list, indices_to_remove, indices_to_keep
-
property
shifted_origin¶ Getter for shift
-
property
space¶ Getter for the current space
-
property
space_attribute¶ Getter for spatial attribute
-
property
streamlines¶ Partially safe getter for streamlines
-
to_center()¶ Safe function to shift streamlines so the center of voxel is the origin
-
to_corner()¶ Safe function to shift streamlines so the corner of voxel is the origin
-
to_rasmm()¶ Safe function to transform streamlines and update state
-
to_vox()¶ Safe function to transform streamlines and update state
-
to_voxmm()¶ Safe function to transform streamlines and update state
ThresholdStoppingCriterion¶
-
class
dipy.workflows.tracking.ThresholdStoppingCriterion¶ Bases:
dipy.tracking.stopping_criterion.StoppingCriterion# Declarations from stopping_criterion.pxd bellow cdef:
double threshold, interp_out_double[1] double[:] interp_out_view = interp_out_view double[:, :, :] metric_map
Methods
check_point
-
__init__($self, /, *args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
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Workflow¶
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class
dipy.workflows.tracking.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
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classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
load_nifti¶
-
dipy.workflows.tracking.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
load_peaks¶
-
dipy.workflows.tracking.load_peaks(fname, verbose=False)¶ Load a PeaksAndMetrics HDF5 file (PAM5)
- Parameters
- fnamestring
Filename of PAM5 file.
- verbosebool
Print summary information about the loaded file.
- Returns
- pamPeaksAndMetrics object
save_tractogram¶
-
dipy.workflows.tracking.save_tractogram(sft, filename, bbox_valid_check=True)¶ Save the stateful tractogram in any format (trk, tck, vtk, fib, dpy)
- Parameters
- sftStatefulTractogram
The stateful tractogram to save
- filenamestring
Filename with valid extension
- Returns
- outputbool
Did the saving work properly
Dpy¶
-
class
dipy.workflows.viz.Dpy(fname, mode='r', compression=0)¶ Bases:
objectMethods
read one track each time
read the entire tractography
read_tracksi(indices)read tracks with specific indices
write_track(track)write on track each time
write_tracks(tracks)write many tracks together
close
version
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__init__(fname, mode='r', compression=0)¶ Advanced storage system for tractography based on HDF5
- Parameters
- fnamestr, full filename
- mode‘r’ read
‘w’ write ‘r+’ read and write only if file already exists
- compression0 no compression to 9 maximum compression
Examples
>>> import os >>> from tempfile import mkstemp #temp file >>> from dipy.io.dpy import Dpy >>> def dpy_example(): ... fd,fname = mkstemp() ... fname += '.dpy'#add correct extension ... dpw = Dpy(fname,'w') ... A=np.ones((5,3)) ... B=2*A.copy() ... C=3*A.copy() ... dpw.write_track(A) ... dpw.write_track(B) ... dpw.write_track(C) ... dpw.close() ... dpr = Dpy(fname,'r') ... dpr.read_track() ... dpr.read_track() ... dpr.read_tracksi([0, 1, 2, 0, 0, 2]) ... dpr.close() ... os.remove(fname) #delete file from disk >>> dpy_example()
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close()¶
-
read_track()¶ read one track each time
-
read_tracks()¶ read the entire tractography
-
read_tracksi(indices)¶ read tracks with specific indices
-
version()¶
-
write_track(track)¶ write on track each time
-
write_tracks(tracks)¶ write many tracks together
-
HorizonFlow¶
-
class
dipy.workflows.viz.HorizonFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
dipy.workflows.workflow.WorkflowMethods
get_io_iterator()Create an iterator for IO.
Return A short name for the workflow used to subdivide.
get_sub_runs()Return No sub runs since this is a simple workflow.
manage_output_overwrite()Check if a file will be overwritten upon processing the inputs.
run(input_files[, cluster, cluster_thr, …])Highly interactive visualization - invert the Horizon!
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
run(input_files, cluster=False, cluster_thr=15.0, random_colors=False, length_gt=0, length_lt=1000, clusters_gt=0, clusters_lt=100000000, native_coords=False, stealth=False, out_dir='', out_stealth_png='tmp.png')¶ Highly interactive visualization - invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy files .nii or .nii.gz. Cluster streamlines on loading.
- Parameters
- input_filesvariable string
- clusterbool
- cluster_thrfloat
- random_colorsbool
- length_gtfloat
- length_ltfloat
- clusters_gtint
- clusters_ltint
- native_coordsbool
- stealthbool
- out_dirstring
- out_stealth_pngstring
References
- Horizon_ISMRM19
Garyfallidis E., M-A. Cote, B.Q. Chandio, S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja, S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and adaptive visualization, Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
-
Workflow¶
-
class
dipy.workflows.viz.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
horizon¶
-
dipy.workflows.viz.horizon(tractograms=None, images=None, pams=None, cluster=False, cluster_thr=15.0, random_colors=False, length_gt=0, length_lt=1000, clusters_gt=0, clusters_lt=10000, world_coords=True, interactive=True, out_png='tmp.png', recorded_events=None)¶ Highly interactive visualization - invert the Horizon!
- Parameters
- tractogramssequence
Sequence of Streamlines objects
- imagessequence of tuples
Each tuple contains data and affine
- pamspeaks
- clusterbool
Enable QuickBundlesX clustering
- cluster_thrfloat
Distance threshold used for clustering
- random_colorsbool
- length_gtfloat
- length_ltfloat
- clusters_gtint
- clusters_ltint
- world_coordsbool
- interactivebool
- out_pngstring
- recorded_eventsstring
File path to replay recorded events
References
- Horizon_ISMRM19
Garyfallidis E., M-A. Cote, B.Q. Chandio, S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja, S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and adaptive visualization, Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
load_nifti¶
-
dipy.workflows.viz.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False)¶
load_peaks¶
-
dipy.workflows.viz.load_peaks(fname, verbose=False)¶ Load a PeaksAndMetrics HDF5 file (PAM5)
- Parameters
- fnamestring
Filename of PAM5 file.
- verbosebool
Print summary information about the loaded file.
- Returns
- pamPeaksAndMetrics object
pjoin¶
-
dipy.workflows.viz.pjoin(a, *p)¶ Join two or more pathname components, inserting ‘/’ as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that ends with a separator.
Workflow¶
-
class
dipy.workflows.workflow.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Bases:
objectMethods
Create an iterator for IO.
Return A short name for the workflow used to subdivide.
Return No sub runs since this is a simple workflow.
Check if a file will be overwritten upon processing the inputs.
run(*args, **kwargs)Execute the workflow.
-
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)¶ Initialize the basic workflow object.
This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.
-
get_io_iterator()¶ Create an iterator for IO.
Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.
-
classmethod
get_short_name()¶ Return A short name for the workflow used to subdivide.
The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.
For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.
Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.
-
get_sub_runs()¶ Return No sub runs since this is a simple workflow.
-
manage_output_overwrite()¶ Check if a file will be overwritten upon processing the inputs.
If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.
-
run(*args, **kwargs)¶ Execute the workflow.
Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)
-
io_iterator_¶
-
dipy.workflows.workflow.io_iterator_(frame, fnc, output_strategy='absolute', mix_names=False)¶ Creates an IOIterator using introspection.
- Parameters
- frameframeobject
Contains the info about the current local variables values.
- fncfunction
The function to inspect
- output_strategystring
Controls the behavior of the IOIterator for output paths.
- mix_namesbool
Whether or not to append a mix of input names at the beginning.
- Returns
- ——-
Properly instantiated IOIterator object.