Denoising using Marchenko-Pastur Principal Component Analysis

Denoising using Marchenko-Pastur Principal Component Analysis

Author: 
Jelle Veraart

"MPPCA": 4d image denoising and noise map estimation by exploiting data redundancy in the PCA domain using universal properties of the eigenspectrum of random covariance matrices, i.e. Marchenko Pastur distribution

  [Signal, Sigma] = MPdenoising(data, mask, kernel, sampling)
       output:
           - Signal: [x, y, z, M] denoised data matrix
           - Sigma: [x, y, z] noise map
       input:
           - data: [x, y, z, M] data matrix
           - mask:   (optional)  region-of-interest [boolean]
           - kernel: (optional)  window size, typically in order of [5 x 5 x 5]
           - sampling: 
                  1. full: sliding window (default for noise map estimation, i.e. [Signal, Sigma] = MPdenoising(...))
                  2. fast: block processing (default for denoising, i.e. [Signal] = MPdenoising(...))
 
 Authors: Jelle Veraart (jelle.veraart@nyumc.org)
 Copyright (c) 2016 New York Universit and University of Antwerp
       
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References

Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping using random matrix theory Magn. Res. Med., 2016, early view, doi: 10.1002/mrm.26059

 

PLEASE NOTE: The software available on this page is provided free of charge and comes without any warranty. CAI²R and the NYU School of Medicine do not take any liability for problems or damage of any kind resulting from the use of the files provided. Operation of the software is solely at the user's own risk. The software developments provided are not medical products and must not be used for making diagnostic decisions.

The software is provided for non-commercial, academic use only. Usage or distribution of the software for commercial purpose is prohibited. All rights belong to the author (Jelle Veraart) and the NYU School of Medicine. If you use the software for academic work, please give credit to the author in publications and cite the related publications.

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We gratefully acknowledge generous support for radiology research at NYU Langone Health from:
 
• The Big George Foundation
• Raymond and Beverly Sackler
• Bernard and Irene Schwartz

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