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# 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 unicode 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)
            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 unicode string)
            output prefix for file names
    recursive: (a boolean)
            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 unicode 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