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# interfaces.nipy.preprocess

## ComputeMask

Link to code

Inputs:

    [Mandatory]
    mean_volume: (an existing file name)
            mean EPI image, used to compute the threshold for the mask

    [Optional]
    M: (a float)
            upper fraction of the histogram to be discarded
    cc: (a boolean)
            Keep only the largest connected component
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    m: (a float)
            lower fraction of the histogram to be discarded
    reference_volume: (an existing file name)
            reference volume used to compute the mask. If none is give, the mean volume is used.

Outputs:

    brain_mask: (an existing file name)

## FmriRealign4d

Link to code

Simultaneous motion and slice timing correction algorithm

This interface wraps nipy’s FmriRealign4d algorithm [1].

### Examples

    >>> from nipype.interfaces.nipy.preprocess import FmriRealign4d
    >>> realigner = FmriRealign4d()
    >>> realigner.inputs.in_file = ['functional.nii']
    >>> realigner.inputs.tr = 2
    >>> realigner.inputs.slice_order = range(0,67)
    >>> res = realigner.run()

### References

[1] | Roche A. A four-dimensional registration algorithm with application to joint correction of motion and slice timing in fMRI. IEEE Trans Med Imaging. 2011 Aug;30(8):1546-54. DOI.

Inputs:

    [Mandatory]
    in_file
            File to realign
    tr: (a float)
            TR in seconds

    [Optional]
    between_loops: (an integer, nipype default value: [5])
            loops used to                                                           realign
            different                                                           runs
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    loops: (an integer, nipype default value: [5])
            loops within each run
    slice_order: (a list of items which are an integer)
            0 based slice order
            requires: time_interp
    speedup: (an integer, nipype default value: [5])
            successive image                                   sub-sampling factors
            for acceleration
    start: (a float, nipype default value: 0.0)
            time offset into TR to align slices to
    time_interp: (True)
            Assume smooth changes across time e.g.,                     fmri series. If you don't
            want slice timing                      correction set this to undefined
            requires: slice_order
    tr_slices: (a float)
            TR slices
            requires: time_interp

Outputs:

    out_file: (an existing file name)
            Realigned files
    par_file: (an existing file name)
            Motion parameter files