.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.dipy.preprocess
==========================


.. _nipype.interfaces.dipy.preprocess.Denoise:


.. index:: Denoise

Denoise
-------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/dipy/preprocess.py#L108>`__

An interface to denoising diffusion datasets [Coupe2008]_.
See
http://nipy.org/dipy/examples_built/denoise_nlmeans.html#example-denoise-nlmeans.

.. [Coupe2008] Coupe P et al., `An Optimized Blockwise Non Local Means
  Denoising Filter for 3D Magnetic Resonance Images
  <http://dx.doi.org/10.1109%2FTMI.2007.906087>`_,
  IEEE Transactions on Medical Imaging, 27(4):425-441, 2008.


Example
~~~~~~~

>>> import nipype.interfaces.dipy as dipy
>>> denoise = dipy.Denoise()
>>> denoise.inputs.in_file = 'diffusion.nii'
>>> denoise.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                The input 4D diffusion-weighted image file
        noise_model: ('rician' or 'gaussian', nipype default value: rician)
                noise distribution model

        [Optional]
        block_radius: (an integer)
                block_radius
        in_mask: (an existing file name)
                brain mask
        noise_mask: (an existing file name)
                mask in which the standard deviation of noise will be computed
        patch_radius: (an integer)
                patch radius

Outputs::

        out_file: (an existing file name)

.. _nipype.interfaces.dipy.preprocess.Resample:


.. index:: Resample

Resample
--------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/dipy/preprocess.py#L47>`__

An interface to reslicing diffusion datasets.
See
http://nipy.org/dipy/examples_built/reslice_datasets.html#example-reslice-datasets.

Example
~~~~~~~

>>> import nipype.interfaces.dipy as dipy
>>> reslice = dipy.Resample()
>>> reslice.inputs.in_file = 'diffusion.nii'
>>> reslice.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                The input 4D diffusion-weighted image file
        interp: (an integer, nipype default value: 1)
                order of the interpolator (0 = nearest, 1 = linear, etc.

        [Optional]
        vox_size: (a tuple of the form: (a float, a float, a float))
                specify the new voxel zooms. If no vox_size is set, then isotropic
                regridding will be performed, with spacing equal to the smallest
                current zoom.

Outputs::

        out_file: (an existing file name)

.. module:: nipype.interfaces.dipy.preprocess


.. _nipype.interfaces.dipy.preprocess.nlmeans_proxy:

:func:`nlmeans_proxy`
---------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/dipy/preprocess.py#L207>`__



Uses non-local means to denoise 4D datasets


.. _nipype.interfaces.dipy.preprocess.resample_proxy:

:func:`resample_proxy`
----------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/dipy/preprocess.py#L171>`__



Performs regridding of an image to set isotropic voxel sizes using dipy.

