Home · Quickstart · User Guide and Examples · Interfaces Index · Developers · About · Nipy

# nipype.interfaces.dipy.preprocess module

## Denoise

Link to code

> Bases: DipyBaseInterface

> 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, 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()

> in_filea pathlike object or string representing an existing file

>   The input 4D diffusion-weighted image file.

> noise_model‘rician’ or ‘gaussian’

>   Noise distribution model. (Nipype default value: `rician`)

> block_radiusan integer

>   Block_radius. (Nipype default value: `5`)

> in_maska pathlike object or string representing an existing file

>   Brain mask.

> noise_maska pathlike object or string representing an existing file

>   Mask in which the standard deviation of noise will be computed.

> patch_radiusan integer

>   Patch radius. (Nipype default value: `1`)

> signal_maska pathlike object or string representing an existing file

>   Mask in which the mean signal will be computed.

> snra float

>   Manually set an SNR.

> out_file : a pathlike object or string representing an existing file

## Resample

Link to code

> Bases: DipyBaseInterface

> 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()

> in_filea pathlike object or string representing an existing file

>   The input 4D diffusion-weighted image file.

> interpan integer

>   Order of the interpolator (0 = nearest, 1 = linear, etc. (Nipype default value: `1`)

> vox_sizea 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.

> out_file : a pathlike object or string representing an existing file

nipype.interfaces.dipy.preprocess.nlmeans_proxy(in_file, settings, snr=None, smask=None, nmask=None, out_file=None)

  Uses non-local means to denoise 4D datasets

nipype.interfaces.dipy.preprocess.resample_proxy(in_file, order=3, new_zooms=None, out_file=None)

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