dipy_fit_dti

Usage

dipy_fit_dti [-h] [–fit_method str] [–b0_threshold float] [–bvecs_tol float] [–sigma float] [–save_metrics [str …]] [–out_dir str] [–out_tensor str]

[–out_fa str] [–out_ga str] [–out_rgb str] [–out_md str] [–out_ad str] [–out_rd str] [–out_mode str] [–out_evec str] [–out_eval str] [–nifti_tensor] input_files bvalues_files bvectors_files mask_files

Workflow for tensor reconstruction and for computing DTI metrics. using Weighted Least-Squares. Performs a tensor reconstruction on the files by ‘globing’ input_files and saves the DTI metrics in a directory specified by out_dir.

Positional Arguments

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.

options:

-h, --help

show this help message and exit

--fit_method str

can be one of the following: ‘WLS’ for weighted least squares ‘LS’ or ‘OLS’ for ordinary least squares ‘NLLS’ for non-linear least-squares ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor fitting

--b0_threshold float

Threshold used to find b0 volumes.

--bvecs_tol float

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

--sigma float

An estimate of the variance. [5] 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.

–save_metrics [str …]

List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval

--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.

Output Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_tensor str

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.

--out_fa str

Name of the fractional anisotropy volume to be saved.

--out_ga str

Name of the geodesic anisotropy volume to be saved.

--out_rgb str

Name of the color fa volume to be saved.

--out_md str

Name of the mean diffusivity volume to be saved.

--out_ad str

Name of the axial diffusivity volume to be saved.

--out_rd str

Name of the radial diffusivity volume to be saved.

--out_mode str

Name of the mode volume to be saved.

--out_evec str

Name of the eigenvectors volume to be saved.

--out_eval str

Name of the eigenvalues to be saved.

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.

[5]

Chang, L-C, Jones, DK and Pierpaoli, C (2005). RESTORE: robust estimation of tensors by outlier rejection. MRM, 53: 1088-95.

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.