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Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging

BACKGROUND: Chemical exchange saturation transfer (CEST) magnetic resonance imaging can provide surrogate biomarkers for disease diagnosis. However, endogenous CEST effects are always diluted and contaminated by competing effects, which results in unwanted signal contributions that lessen the specif...

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Autores principales: Zhao, Yingcheng, Wang, Xiaoli, Wang, Yifei, Wang, Beilei, Zhang, Lihong, Wei, Xiao, He, Xiaowei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622448/
https://www.ncbi.nlm.nih.gov/pubmed/36330187
http://dx.doi.org/10.21037/qims-22-313
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author Zhao, Yingcheng
Wang, Xiaoli
Wang, Yifei
Wang, Beilei
Zhang, Lihong
Wei, Xiao
He, Xiaowei
author_facet Zhao, Yingcheng
Wang, Xiaoli
Wang, Yifei
Wang, Beilei
Zhang, Lihong
Wei, Xiao
He, Xiaowei
author_sort Zhao, Yingcheng
collection PubMed
description BACKGROUND: Chemical exchange saturation transfer (CEST) magnetic resonance imaging can provide surrogate biomarkers for disease diagnosis. However, endogenous CEST effects are always diluted and contaminated by competing effects, which results in unwanted signal contributions that lessen the specificity of CEST to underlying biochemical exchange processes. The aim of this study was to examine a method for the accurate quantification of CEST effects. METHODS: A Markov chain Monte Carlo (MCMC)-based Bayesian inference approach was proposed to estimate the exchange parameters, and CEST effects could be fitted using these estimations. This approach was tested in Bloch simulation and ischemic stroke rat experiments, and its performance was evaluated using quantification maps and numerical metrics. RESULTS: With 12 groups of simulations, the MCMC method achieved satisfactory fittings on both 2-pool and 5-pool models. The sum of squares error values and the root mean square error of the fitted Z-spectra were smaller than 10(−3), and the coefficient of determination (R-squared) values were close to 1. The corresponding CEST quantification [Formula: see text] spectra were also well fitted and successfully separated the mixed CEST effects. The estimated parameters showed little bias relative to the ground truth, with errors between the true and estimated values of each parameter of less than 0.5%. In the animal experiments, [Formula: see text] fitted using the MCMC method showed obvious contrast between ischemic and contralateral regions at the early stage. Compared with other quantification methods, it displayed the highest contrast-to-noise ratios (3.9, 2.73, and 3.93) and the lowest coefficient of variation values (0.181, 0.2224, and 0.2897) in all three stroke periods. CONCLUSIONS: The MCMC method provided an efficient approach for parameter estimation and CEST effect quantification. The method may therefore be useful in achieving an accurate pathological diagnosis.
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spelling pubmed-96224482022-11-02 Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging Zhao, Yingcheng Wang, Xiaoli Wang, Yifei Wang, Beilei Zhang, Lihong Wei, Xiao He, Xiaowei Quant Imaging Med Surg Original Article BACKGROUND: Chemical exchange saturation transfer (CEST) magnetic resonance imaging can provide surrogate biomarkers for disease diagnosis. However, endogenous CEST effects are always diluted and contaminated by competing effects, which results in unwanted signal contributions that lessen the specificity of CEST to underlying biochemical exchange processes. The aim of this study was to examine a method for the accurate quantification of CEST effects. METHODS: A Markov chain Monte Carlo (MCMC)-based Bayesian inference approach was proposed to estimate the exchange parameters, and CEST effects could be fitted using these estimations. This approach was tested in Bloch simulation and ischemic stroke rat experiments, and its performance was evaluated using quantification maps and numerical metrics. RESULTS: With 12 groups of simulations, the MCMC method achieved satisfactory fittings on both 2-pool and 5-pool models. The sum of squares error values and the root mean square error of the fitted Z-spectra were smaller than 10(−3), and the coefficient of determination (R-squared) values were close to 1. The corresponding CEST quantification [Formula: see text] spectra were also well fitted and successfully separated the mixed CEST effects. The estimated parameters showed little bias relative to the ground truth, with errors between the true and estimated values of each parameter of less than 0.5%. In the animal experiments, [Formula: see text] fitted using the MCMC method showed obvious contrast between ischemic and contralateral regions at the early stage. Compared with other quantification methods, it displayed the highest contrast-to-noise ratios (3.9, 2.73, and 3.93) and the lowest coefficient of variation values (0.181, 0.2224, and 0.2897) in all three stroke periods. CONCLUSIONS: The MCMC method provided an efficient approach for parameter estimation and CEST effect quantification. The method may therefore be useful in achieving an accurate pathological diagnosis. AME Publishing Company 2022-11 /pmc/articles/PMC9622448/ /pubmed/36330187 http://dx.doi.org/10.21037/qims-22-313 Text en 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhao, Yingcheng
Wang, Xiaoli
Wang, Yifei
Wang, Beilei
Zhang, Lihong
Wei, Xiao
He, Xiaowei
Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title_full Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title_fullStr Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title_full_unstemmed Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title_short Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
title_sort application of a markov chain monte carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622448/
https://www.ncbi.nlm.nih.gov/pubmed/36330187
http://dx.doi.org/10.21037/qims-22-313
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