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# 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 (int or long)

>   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 unicode 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 (int or long)

>   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 (int or long)

>   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 unicode 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 (int or long)

>   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 (int or long)

>   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 unicode 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.