MP-PCA denoising for diffusion MRS data: promises and pitfalls.
Détails
Etat: Public
Version: Final published version
Licence: CC BY-NC-ND 4.0
ID Serval
serval:BIB_109056986571
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
MP-PCA denoising for diffusion MRS data: promises and pitfalls.
Périodique
NeuroImage
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
11/2022
Peer-reviewed
Oui
Volume
263
Pages
119634
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: 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 <sub>0</sub> drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.
Mots-clé
Animals, Rats, Humans, Principal Component Analysis, Diffusion Magnetic Resonance Imaging/methods, Magnetic Resonance Spectroscopy, Brain/diagnostic imaging, Signal-To-Noise Ratio, Algorithms, Marchenko-Pastur, PCA, brain, denoising, diffusion-weighted MRS
Pubmed
Web of science
Open Access
Oui
Financement(s)
Fonds national suisse / Carrières / PCEFP2_194260
Création de la notice
04/10/2022 11:07
Dernière modification de la notice
27/10/2023 7:11