nipype.interfaces.dipy.preprocess module¶
Denoise¶
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¶
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.