dipy_fit_msmtcsd

Synopsis

Constrained spherical deconvolution.

See [1] for further details about the method. See [2] if you use the MSMT method. This method extends the SSST-CSD introduced in [1].

Usage

dipy_fit_msmtcsd [OPTIONS] input_files bvalues_files bvectors_files mask_files

Input Parameters

• input_files

  Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

• bvalues_files

  Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

• bvectors_files

  Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

• mask_files

  Path to the input masks. This path may contain wildcards to use multiple masks at once.

General Options

• --use_msmt

  If True, use the Multi-Shell Multi-Tissue method. (default: False)

• --t1_file

  Path to the T1 file. If not available, an anisotropic map will be computed. Option available only for --use_msmt or use_msmt=True. (default: None)

• --wm_file

  Path to the WM mask. If not provided, HMRF tissue classifier will be used on the T1 image to obtain the WM mask. Option available only for --use_msmt or use_msmt=True (default: None)

• --gm_file

  Path to the GM mask. If not provided, HMRF tissue classifier will be used on the T1 image to obtain the GM mask. Option available only for --use_msmt or use_msmt=True (default: None)

• --csf_file

  Path to the CSF mask. If not provided, HMRF tissue classifier will be used on the T1 image to obtain the CSF mask. Option available only for --use_msmt or use_msmt=True (default: None)

• --iso

  Number of isotropic compartments for running the MSMT-CSD. Currently fixed at 3 (WM, GM, CSF); values below 3 are rejected. (default: 3)

• --beta

  The smoothness factor of the tissue segmentation during MSMT-CSD. Good performance is achieved with values between 0 and 0.5. (default: 0.1)

• --bval_tol

  Tolerance gap for b-values clustering in MSMT-CSD. (default: 20)

• --b0_threshold

  Threshold used to find b0 volumes. (default: 50.0)

• --bvecs_tol

  Bvecs should be unit vectors. (default: 0.01)

• --roi_center

  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_radii

  radii of cuboid ROI in voxels. (default: 10)

• --fa_thr

  FA threshold for calculating the response function. (default: 0.7)

• --frf

  Fiber response function can be for example inputted 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)

• --sphere_name

  Sphere name on which to reconstruct the fODFs. (default: None)

• --relative_peak_threshold

  Only return peaks greater than relative_peak_threshold * m where m is the largest peak. (default: 0.5)

• --min_separation_angle

  The minimum distance between directions. If two peaks are too close only the larger of the two is returned. (default: 25)

• --sh_order_max

  Spherical harmonics order (l) used in the CSA fit. (default: 8)

• --parallel

  Whether to use parallelization in peak-finding during the calibration procedure. (default: False)

• --extract_pam_values

  Save or not to save pam volumes as single nifti files. (default: False)

• --num_processes

  If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error. (default: None)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_pam

  Name of the peaks volume to be saved. (default: peaks.pam5)

• --out_shm

  Name of the spherical harmonics volume to be saved. (default: shm.nii.gz)

• --out_peaks_dir

  Name of the peaks directions volume to be saved. (default: peaks_dirs.nii.gz)

• --out_peaks_values

  Name of the peaks values volume to be saved. (default: peaks_values.nii.gz)

• --out_peaks_indices

  Name of the peaks indices volume to be saved. (default: peaks_indices.nii.gz)

• --out_gfa

  Name of the generalized FA volume to be saved. (default: gfa.nii.gz)

• --out_sphere

  Sphere vertices name to be saved. (default: sphere.txt)

• --out_b

  Name of the B Matrix to be saved. (default: B.nii.gz)

• --out_qa

  Name of the Quantitative Anisotropy to be saved. (default: qa.nii.gz)

References

[1] (1,2)

Jacques-Donald Tournier, Fernando Calamante, and Alan Connelly. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 35(4):1459–1472, May 2007. URL: https://doi.org/10.1016/j.neuroimage.2012.06.005, doi:10.1016/j.neuroimage.2007.02.016.

[2]

Ben Jeurissen, Jacques-Donald Tournier, Thijs Dhollander, Alan Connelly, and Jan Sijbers. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 103:411–426, December 2014. URL: https://doi.org/10.1016/j.neuroimage.2014.07.061, doi:10.1016/j.neuroimage.2014.07.061.

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.