MP-PCA denoising for diffusion MRS data: promises and pitfalls.
Marchenko-Pastur
PCA
brain
denoising
diffusion-weighted MRS
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
received:
05
05
2022
revised:
07
09
2022
accepted:
14
09
2022
medline:
5
5
2023
pubmed:
24
9
2022
entrez:
23
9
2022
Statut:
ppublish
Résumé
Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B
Identifiants
pubmed: 36150605
pii: S1053-8119(22)00749-2
doi: 10.1016/j.neuroimage.2022.119634
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
119634Informations de copyright
Copyright © 2022. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors have no conflict of interest to declare.