nipype.interfaces.dipy.reconstruction module¶
Interfaces to the reconstruction algorithms in dipy
CSD¶
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¶
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¶
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.