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() # doctest: +SKIP
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.
b0_thres : an integer (int or long)
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 unicode 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 (int or long)
Maximal shperical harmonics order. (Nipype default value: 8)
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() # doctest: +SKIP
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.
auto : a boolean
Use the auto_response estimator from dipy. Mutually exclusive with inputs: recursive.
b0_thres : an integer (int or long)
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 unicode 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 (int or long)
ROI radius to be used in auto_response. (Nipype default value: 10)
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() # doctest: +SKIP
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.
b0_thres : an integer (int or long)
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 unicode string
Output prefix for file names.
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.