ArticleQuantitative imaging in medicine and surgery2022
Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mamba-AE: pixel-wise anomaly detection auto-encoder in chemical exchange saturation transfer magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Determining hexavalent chromium transport properties in alkaline nuclear waste using nuclear magnetic resonance spectroscopy.Communications chemistry · 2025Article
- Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2023Article
- Novel Nanoprobe with Combined Ultrasonography/Chemical Exchange Saturation Transfer Magnetic Resonance Imaging for Precise Diagnosis of Tumors.Pharmaceutics · 2023Article
- Hierarchical K-means clustering method for accelerated Lorentzian estimation (KALE) in chemical exchange saturation transfer-magnetic resonance imaging quantification.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
7 authors.
Funding
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Abstract
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 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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