dipy_denoise_patch2self

Synopsis

Workflow for Patch2Self denoising method.

See [1] for further details about the method. See [2] for further details about the new method. It applies patch2self denoising [1] on each file found by ‘globing’ input_file and bval_file. It saves the results in a directory specified by out_dir.

Usage

dipy_denoise_patch2self [OPTIONS] input_files bval_files model

Input Parameters

• 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.

• model

  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’.

General Options

• --b0_threshold

  Threshold for considering volumes as b0.

• --alpha

  Regularization parameter only for ridge regression model.

• --verbose

  Show progress of Patch2Self and time taken.

• --patch_radius

  The radius of the local patch to be taken around each voxel

• --b0_denoising

  Skips denoising b0 volumes if set to False.

• --clip_negative_vals

  Sets negative values after denoising to 0 using np.clip.

• --shift_intensity

  Shifts the distribution of intensities per volume to give non-negative values

• --ver

  Version of the Patch2Self algorithm to use between 1 or 3.

Output Options

• --out_dir

  Output directory (default current directory)

• --out_denoised

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

References

[1] (1,2)

Shreyas Fadnavis, Joshua Batson, and Eleftherios Garyfallidis. Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning. In Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems, volume 33, 16293–16303. Curran Associates, Inc., 2020. URL: https://proceedings.neurips.cc/paper_files/paper/2020/file/bc047286b224b7bfa73d4cb02de1238d-Paper.pdf.

[2]

Shreyas Fadnavis, Agniva Chowdhury, Joshua Batson, Petros Drineas, and Eleftherios Garyfallidis. Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, volume, 27641–27651. 2024. URL: https://openaccess.thecvf.com/content/CVPR2024/papers/Fadnavis_Patch2Self2_Self-supervised_Denoising_on_Coresets_via_Matrix_Sketching_CVPR_2024_paper.pdf.

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.