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()
in_bvala pathlike object or string representing an existing file

Input b-values table.

in_bveca pathlike object or string representing an existing file

Input b-vectors table.

in_filea pathlike object or string representing an existing file

Input diffusion data.

b0_thresan integer

B0 threshold. (Nipype default value: 700)

in_maska pathlike object or string representing an existing file

Input mask in which compute tensors.

out_fodsa pathlike object or string representing a file

FODFs output file name.

out_prefixa string

Output prefix for file names.

responsea pathlike object or string representing an existing file

Single fiber estimated response.

save_fodsa boolean

Save fODFs in file. (Nipype default value: True)

sh_orderan integer

Maximal shperical harmonics order. (Nipype default value: 8)

modela pathlike object or string representing a file

Python pickled object of the CSD model fitted.

out_fodsa 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()
in_bvala pathlike object or string representing an existing file

Input b-values table.

in_bveca pathlike object or string representing an existing file

Input b-vectors table.

in_evalsa pathlike object or string representing an existing file

Input eigenvalues file.

in_filea pathlike object or string representing an existing file

Input diffusion data.

autoa boolean

Use the auto_response estimator from dipy. Mutually exclusive with inputs: recursive.

b0_thresan integer

B0 threshold. (Nipype default value: 700)

fa_thresha float

FA threshold. (Nipype default value: 0.7)

in_maska pathlike object or string representing an existing file

Input mask in which we find single fibers.

out_maska pathlike object or string representing a file

Computed wm mask. (Nipype default value: wm_mask.nii.gz)

out_prefixa string

Output prefix for file names.

recursivea boolean

Use the recursive response estimator from dipy. Mutually exclusive with inputs: auto.

responsea pathlike object or string representing a file

The output response file. (Nipype default value: response.txt)

roi_radiusan integer

ROI radius to be used in auto_response. (Nipype default value: 10)

out_maska pathlike object or string representing an existing file

Output wm mask.

responsea 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()
in_bvala pathlike object or string representing an existing file

Input b-values table.

in_bveca pathlike object or string representing an existing file

Input b-vectors table.

in_filea pathlike object or string representing an existing file

Input diffusion data.

b0_thresan integer

B0 threshold. (Nipype default value: 700)

in_maska pathlike object or string representing an existing file

Input mask in which compute tensors.

noise_maska pathlike object or string representing an existing file

Input mask in which compute noise variance.

out_prefixa string

Output prefix for file names.

evalsa pathlike object or string representing a file

Output the eigenvalues of the fitted DTI.

evecsa pathlike object or string representing a file

Output the eigenvectors of the fitted DTI.

faa pathlike object or string representing a file

Output fractional anisotropy (FA) map computed from the fitted DTI.

mda pathlike object or string representing a file

Output mean diffusivity (MD) map computed from the fitted DTI.

modea pathlike object or string representing a file

Output mode (MO) map computed from the fitted DTI.

rda pathlike object or string representing a file

Output radial diffusivity (RD) map computed from the fitted DTI.

tracea pathlike object or string representing a file

Output the tensor trace map computed from the fitted DTI.