dipy_denoise_patch2self

Usage

dipy_denoise_patch2self [-h] [–model str] [–verbose] [–out_dir str] [–out_denoised str] input_files bval_files

Workflow for Patch2Self denoising method.

It applies patch2self denoising on each file found by ‘globing’ input_file and bval_file. It saves the results in a directory specified by out_dir.

Positional Arguments

input_files Path to the input volumes. This path may contain wildcards to process multiple inputs at once. bval_files bval file associated with the diffusion data.

Optional Arguments

-h, --help

show this help message and exit

--model str

This will determine the algorithm used to solve the set of linear equations underlying this model. If it is a string it needs to be one of the following: {‘ols’, ‘ridge’, ‘lasso’}. Otherwise, it can be an object that inherits from dipy.optimize.SKLearnLinearSolver or an object with a similar interface from Scikit-Learn: sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso or sklearn.linear_model.Ridge and other objects that inherit from sklearn.base.RegressorMixin. Default: ‘ols’.

--verbose

Show progress of Patch2Self and time taken.

Output Arguments(Optional)

--out_dir str

Output directory (default current directory)

--out_denoised str

Name of the resulting denoised volume (default: dwi_patch2self.nii.gz)

References

Fadnavis20

19. Fadnavis, J. Batson, E. Garyfallidis, Patch2Self: Denoising Diffusion MRI with Self-supervised Learning, Advances in Neural Information Processing Systems 33 (2020)

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.