interfaces.dipy.reconstruction

CSD

Link to code

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() 

Inputs:

[Mandatory]
in_bval: (an existing file name)
        input b-values table
in_bvec: (an existing file name)
        input b-vectors table
in_file: (an existing file name)
        input diffusion data

[Optional]
b0_thres: (an integer (int or long), nipype default value: 700)
        b0 threshold
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
in_mask: (an existing file name)
        input mask in which compute tensors
out_fods: (a file name)
        fODFs output file name
out_prefix: (a string)
        output prefix for file names
response: (an existing file name)
        single fiber estimated response
save_fods: (a boolean, nipype default value: True)
        save fODFs in file
sh_order: (an integer (int or long), nipype default value: 8)
        maximal shperical harmonics order

Outputs:

model: (a file name)
        Python pickled object of the CSD model fitted.
out_fods: (a file name)
        fODFs output file name

EstimateResponseSH

Link to code

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() 

Inputs:

[Mandatory]
in_bval: (an existing file name)
        input b-values table
in_bvec: (an existing file name)
        input b-vectors table
in_evals: (an existing file name)
        input eigenvalues file
in_file: (an existing file name)
        input diffusion data

[Optional]
auto: (a boolean, nipype default value: True)
        use the auto_response estimator from dipy
        mutually_exclusive: recursive
b0_thres: (an integer (int or long), nipype default value: 700)
        b0 threshold
fa_thresh: (a float, nipype default value: 0.7)
        FA threshold
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
in_mask: (an existing file name)
        input mask in which we find single fibers
out_mask: (a file name, nipype default value: wm_mask.nii.gz)
        computed wm mask
out_prefix: (a string)
        output prefix for file names
recursive: (a boolean, nipype default value: False)
        use the recursive response estimator from dipy
        mutually_exclusive: auto
response: (a file name, nipype default value: response.txt)
        the output response file
roi_radius: (an integer (int or long), nipype default value: 10)
        ROI radius to be used in auto_response

Outputs:

out_mask: (an existing file name)
        output wm mask
response: (an existing file name)
        the response file

RESTORE

Link to code

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() 

Inputs:

[Mandatory]
in_bval: (an existing file name)
        input b-values table
in_bvec: (an existing file name)
        input b-vectors table
in_file: (an existing file name)
        input diffusion data

[Optional]
b0_thres: (an integer (int or long), nipype default value: 700)
        b0 threshold
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
in_mask: (an existing file name)
        input mask in which compute tensors
noise_mask: (an existing file name)
        input mask in which compute noise variance
out_prefix: (a string)
        output prefix for file names

Outputs:

evals: (a file name)
        output the eigenvalues of the fitted DTI
evecs: (a file name)
        output the eigenvectors of the fitted DTI
fa: (a file name)
        output fractional anisotropy (FA) map computed from the fitted DTI
md: (a file name)
        output mean diffusivity (MD) map computed from the fitted DTI
mode: (a file name)
        output mode (MO) map computed from the fitted DTI
rd: (a file name)
        output radial diffusivity (RD) map computed from the fitted DTI
trace: (a file name)
        output the tensor trace map computed from the fitted DTI