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() 
Mandatory Inputs:
  • in_file (a 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)

Optional Inputs:
  • block_radius (an integer) – Block_radius. (Nipype default value: 5)

  • in_mask (a pathlike object or string representing an existing file) – Brain mask.

  • noise_mask (a pathlike object or string representing an existing file) – Mask in which the standard deviation of noise will be computed.

  • patch_radius (an integer) – Patch radius. (Nipype default value: 1)

  • signal_mask (a pathlike object or string representing an existing file) – Mask in which the mean signal will be computed.

  • snr (a float) – Manually set an SNR.

Outputs:

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() 
Mandatory Inputs:
  • in_file (a pathlike object or string representing an existing file) – The input 4D diffusion-weighted image file.

  • interp (an integer) – Order of the interpolator (0 = nearest, 1 = linear, etc. (Nipype default value: 1)

Optional Inputs:

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 (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.