nipype.interfaces.dipy.reconstruction module

Interfaces to the reconstruction algorithms in dipy

CSD

Link to code

Bases: DipyDiffusionInterface

Uses CSD [Tournier2007] to generate the fODF of DWIs. The interface uses dipy, as explained in dipy’s CSD example.

[Tournier2007]

Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

Example

>>> from nipype.interfaces import dipy as ndp
>>> csd = ndp.CSD()
>>> csd.inputs.in_file = '4d_dwi.nii'
>>> csd.inputs.in_bval = 'bvals'
>>> csd.inputs.in_bvec = 'bvecs'
>>> res = csd.run()
Mandatory Inputs:

  • in_bval (a pathlike object or string representing an existing file) – Input b-values table.

  • in_bvec (a pathlike object or string representing an existing file) – Input b-vectors table.

  • in_file (a pathlike object or string representing an existing file) – Input diffusion data.

Optional Inputs:

  • b0_thres (an integer) – B0 threshold. (Nipype default value: 700)

  • in_mask (a pathlike object or string representing an existing file) – Input mask in which compute tensors.

  • out_fods (a pathlike object or string representing a file) – FODFs output file name.

  • out_prefix (a string) – Output prefix for file names.

  • response (a pathlike object or string representing an existing file) – Single fiber estimated response.

  • save_fods (a boolean) – Save fODFs in file. (Nipype default value: True)

  • sh_order (an integer) – Maximal shperical harmonics order. (Nipype default value: 8)

Outputs:

  • model (a pathlike object or string representing a file) – Python pickled object of the CSD model fitted.

  • out_fods (a pathlike object or string representing a file) – FODFs output file name.

EstimateResponseSH

Link to code

Bases: DipyDiffusionInterface

Uses dipy to compute the single fiber response to be used in spherical deconvolution methods, in a similar way to MRTrix’s command estimate_response.

Example

>>> from nipype.interfaces import dipy as ndp
>>> dti = ndp.EstimateResponseSH()
>>> dti.inputs.in_file = '4d_dwi.nii'
>>> dti.inputs.in_bval = 'bvals'
>>> dti.inputs.in_bvec = 'bvecs'
>>> dti.inputs.in_evals = 'dwi_evals.nii'
>>> res = dti.run()
Mandatory Inputs:

  • in_bval (a pathlike object or string representing an existing file) – Input b-values table.

  • in_bvec (a pathlike object or string representing an existing file) – Input b-vectors table.

  • in_evals (a pathlike object or string representing an existing file) – Input eigenvalues file.

  • in_file (a pathlike object or string representing an existing file) – Input diffusion data.

Optional Inputs:

  • auto (a boolean) – Use the auto_response estimator from dipy. Mutually exclusive with inputs: recursive.

  • b0_thres (an integer) – B0 threshold. (Nipype default value: 700)

  • fa_thresh (a float) – FA threshold. (Nipype default value: 0.7)

  • in_mask (a pathlike object or string representing an existing file) – Input mask in which we find single fibers.

  • out_mask (a pathlike object or string representing a file) – Computed wm mask. (Nipype default value: wm_mask.nii.gz)

  • out_prefix (a string) – Output prefix for file names.

  • recursive (a boolean) – Use the recursive response estimator from dipy. Mutually exclusive with inputs: auto.

  • response (a pathlike object or string representing a file) – The output response file. (Nipype default value: response.txt)

  • roi_radius (an integer) – ROI radius to be used in auto_response. (Nipype default value: 10)

Outputs:

  • out_mask (a pathlike object or string representing an existing file) – Output wm mask.

  • response (a pathlike object or string representing an existing file) – The response file.

RESTORE

Link to code

Bases: DipyDiffusionInterface

Uses RESTORE [Chang2005] to perform DTI fitting with outlier detection. The interface uses dipy, as explained in dipy’s documentation.

[Chang2005]

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

Example

>>> from nipype.interfaces import dipy as ndp
>>> dti = ndp.RESTORE()
>>> dti.inputs.in_file = '4d_dwi.nii'
>>> dti.inputs.in_bval = 'bvals'
>>> dti.inputs.in_bvec = 'bvecs'
>>> res = dti.run()
Mandatory Inputs:

  • in_bval (a pathlike object or string representing an existing file) – Input b-values table.

  • in_bvec (a pathlike object or string representing an existing file) – Input b-vectors table.

  • in_file (a pathlike object or string representing an existing file) – Input diffusion data.

Optional Inputs:

  • b0_thres (an integer) – B0 threshold. (Nipype default value: 700)

  • in_mask (a pathlike object or string representing an existing file) – Input mask in which compute tensors.

  • noise_mask (a pathlike object or string representing an existing file) – Input mask in which compute noise variance.

  • out_prefix (a string) – Output prefix for file names.

Outputs:

  • evals (a pathlike object or string representing a file) – Output the eigenvalues of the fitted DTI.

  • evecs (a pathlike object or string representing a file) – Output the eigenvectors of the fitted DTI.

  • fa (a pathlike object or string representing a file) – Output fractional anisotropy (FA) map computed from the fitted DTI.

  • md (a pathlike object or string representing a file) – Output mean diffusivity (MD) map computed from the fitted DTI.

  • mode (a pathlike object or string representing a file) – Output mode (MO) map computed from the fitted DTI.

  • rd (a pathlike object or string representing a file) – Output radial diffusivity (RD) map computed from the fitted DTI.

  • trace (a pathlike object or string representing a file) – Output the tensor trace map computed from the fitted DTI.