dipy_fit_dti

Synopsis

Workflow for tensor reconstruction and for computing DTI metrics using Weighted Least-Squares.

Performs a tensor reconstruction [1], [2] on the files by ‘globing’ input_files and saves the DTI metrics in a directory specified by out_dir.

Usage

dipy_fit_dti [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

• --fit_method

  can be one of the following: ‘WLS’ for weighted least squares [3] ‘LS’ or ‘OLS’ for ordinary least squares [3] ‘NLLS’ for non-linear least-squares ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor fitting [4]. (default: WLS)

• --b0_threshold

  Threshold used to find b0 volumes. (default: 50)

• --bvecs_tol

  Threshold used to check that norm(bvec) = 1 +/- bvecs_tol (default: 0.01)

• --npeaks

  Number of peaks/eigen vectors to save in each voxel. DTI generates 3 eigen values and eigen vectors. The principal eigenvector is saved by default. (default: 1)

• --sigma

  An estimate of the variance. Chang et al.[4] recommend to use 1.5267 * std(background_noise), where background_noise is estimated from some part of the image known to contain no signal (only noise) b-vectors are unit vectors. (default: None)

• --save_metrics

  List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval (default: None)

• --nifti_tensor

  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)

• --extract_pam_values

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

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_tensor

  Name of the tensors volume to be saved. 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. (default: tensors.nii.gz)

• --out_fa

  Name of the fractional anisotropy volume to be saved. (default: fa.nii.gz)

• --out_ga

  Name of the geodesic anisotropy volume to be saved. (default: ga.nii.gz)

• --out_rgb

  Name of the color fa volume to be saved. (default: rgb.nii.gz)

• --out_md

  Name of the mean diffusivity volume to be saved. (default: md.nii.gz)

• --out_ad

  Name of the axial diffusivity volume to be saved. (default: ad.nii.gz)

• --out_rd

  Name of the radial diffusivity volume to be saved. (default: rd.nii.gz)

• --out_mode

  Name of the mode volume to be saved. (default: mode.nii.gz)

• --out_evec

  Name of the eigenvectors volume to be saved. (default: evecs.nii.gz)

• --out_eval

  Name of the eigenvalues to be saved. (default: evals.nii.gz)

• --out_pam

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

• --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_sphere

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

• --out_qa

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

References

[1]

Peter J. Basser, James Mattiello, and Denis Le Bihan. Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B, 103(3):247–254, March 1994. URL: https://doi.org/10.1006/jmrb.1994.1037, doi:10.1006/jmrb.1994.1037.

[2]

Peter J. Basser and Carlo Pierpaoli. Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI. Journal of Magnetic Resonance, Series B, 111(3):209–219, 1996. URL: https://doi.org/10.1006/jmrb.1996.0086, doi:10.1006/jmrb.1996.0086.

[3] (1,2)

SungWon Chung, Ying Lu, and Roland G. Henry. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage, 33(2):531–541, 2006. URL: https://doi.org/10.1016/j.neuroimage.2006.07.001, doi:10.1016/j.neuroimage.2006.07.001.

[4] (1,2)

Lin-Ching Chang, Derek K. Jones, and Carlo Pierpaoli. RESTORE: Robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine, 53(5):1088–1095, 2005. URL: https://doi.org/10.1002/mrm.20426, doi:10.1002/mrm.20426.

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.