interfaces.dipy.preprocess¶
Denoise¶
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()
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 (int or long))
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 (int or long))
patch radius
signal_mask: (an existing file name)
mask in which the mean signal will be computed
snr: (a float)
manually set an SNR
Outputs:
out_file: (an existing file name)
Resample¶
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()
Inputs:
[Mandatory]
in_file: (an existing file name)
The input 4D diffusion-weighted image file
interp: (an integer (int or long), 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)
resample_proxy()¶
Performs regridding of an image to set isotropic voxel sizes using dipy.