dipy_fit_dki

Synopsis

Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics.

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

Usage

dipy_fit_dki [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 bvalues files. This path may contain wildcards to use multiple bvalues files at once.

• mask_files

  Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

General Options

• --fit_method

  can be one of the following: ‘OLS’ or ‘ULLS’ for ordinary least squares ‘WLS’ or ‘UWLLS’ for weighted ordinary least squares (default: WLS)

• --b0_threshold

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

• --sigma

  An estimate of the variance. Chang et al.[3] 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) (default: None)

• --save_metrics

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

• --extract_pam_values

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

• --npeaks

  Number of peaks to fit in each voxel. (default: 5)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_dt_tensor

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

• --out_dk_tensor

  Name of the tensors volume to be saved. (default: dki_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_mk

  Name of the mean kurtosis to be saved. (default: mk.nii.gz)

• --out_ak

  Name of the axial kurtosis to be saved. (default: ak.nii.gz)

• --out_rk

  Name of the radial kurtosis to be saved. (default: rk.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)

References

[1]

Ali Tabesh, Jens H. Jensen, Babak A. Ardekani, and Joseph A. Helpern. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magnetic Resonance in Medicine, 65(3):823–836, 2011. URL: https://doi.org/10.1002/mrm.22655, doi:10.1002/mrm.22655.

[2]

Jens H. Jensen, Joseph A. Helpern, Anita Ramani, Hanzhang Lu, and Kyle Kaczynski. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic Resonance in Medicine, 53(6):1432–1440, 2005. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.20508, doi:https://doi.org/10.1002/mrm.20508.

[3]

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.