PEAKIT: A Gaussian Process regression analysis tool for chemical exchange saturation transfer spectra.

Chemical Exchange Saturation Transfer (CEST) Gaussian process regression Noise level Peak detection Python Tkinter User interface Z-spectrum

Journal

Journal of magnetic resonance (San Diego, Calif. : 1997)
ISSN: 1096-0856
Titre abrégé: J Magn Reson
Pays: United States
ID NLM: 9707935

Informations de publication

Date de publication:
01 2022
Historique:
received: 08 10 2021
revised: 26 11 2021
accepted: 30 11 2021
pubmed: 16 12 2021
medline: 27 1 2022
entrez: 15 12 2021
Statut: ppublish

Résumé

Chemical Exchange Saturation Transfer (CEST) is a powerful technique for metabolic imaging, capable of exploring concentrations in the μM to mM range. However, extracting quantitative information from Z-spectra can be challenging due to the non-CEST contributions present and the limited knowledge about the exchanging pools. The PEAKIT tool is proposed as an alternative approach to quantifying CEST peaks, which requires no prior assumptions about the frequency offset or the underlying shape of the baseline. Specifically, the tool takes as input an experimental Z-spectrum and proceeds to identify peak candidates. After a baseline estimation based on Gaussian Process regression, PEAKIT outputs the chemical shift offsets, the areas, the heights and the statistical significance of the detected peaks. The performance and limitations of the PEAKIT tool are discussed for in vitro and in vivo applications.

Identifiants

pubmed: 34906779
pii: S1090-7807(21)00211-1
doi: 10.1016/j.jmr.2021.107122
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

107122

Informations de copyright

Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Michele Lecis (M)

NeuroSpin, CEA, Gif-sur-Yvette, Paris-Saclay University, Saclay, France; Technical University of Munich, Munich, Germany.

Solène Bardin (S)

NeuroSpin, CEA, Gif-sur-Yvette, Paris-Saclay University, Saclay, France.

Catalin I Ciobanu (CI)

Boostrs SAS, Paris, France.

Luisa Ciobanu (L)

NeuroSpin, CEA, Gif-sur-Yvette, Paris-Saclay University, Saclay, France. Electronic address: luisa.ciobanu@cea.fr.

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Classifications MeSH