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Optshrink LR+S: Accelerated fMRI Reconstruction using Non-Convex Optimal Singular Value Shrinkage

version 1.0 (12.2 KB) by Priya Aggarwal
Optshrink LR+S method for fMRI reconstruction from undersampled k-t space data.

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Updated 20 Dec 2016

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This code implements Optshrink LR+S algorithm for fMRI data reconstruction as described in the paper, "Optshrink LR+S: Accelerated fMRI Reconstruction using Non-Convex Optimal Singular Value Shrinkage", Brain Informatics, December 2016.
The proposed method reconstructs undersampled fMRI data using a linear combination of low rank and sparse components. The low rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l-1 minimization. The
proposed method estimates low rank component with denoising, hence, improve fMRI reconstruction accuracy.

Cite As

Priya Aggarwal (2020). Optshrink LR+S: Accelerated fMRI Reconstruction using Non-Convex Optimal Singular Value Shrinkage (https://www.mathworks.com/matlabcentral/fileexchange/60836-optshrink-lr-s-accelerated-fmri-reconstruction-using-non-convex-optimal-singular-value-shrinkage), MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R2014b
Compatible with any release
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