Quantitative Bayesian model-based analysis of amide proton transfer MRI.

Journal article

Amide Proton Transfer (APT) reports on contrast derived from the exchange of protons between amide groups and water. Commonly, APT contrast is quantified by asymmetry analysis, providing an ensemble contrast of both amide proton concentration and exchange rate. An alternative is to sample the off-resonant spectrum and fit an exchange model, permitting the APT effect to be quantified, correcting automatically for confounding effects of spillover, field inhomogeneity, and magnetization transfer. Additionally, it should permit amide concentration and exchange rate to be independently quantified. Here, a Bayesian method is applied to this problem allowing pertinent prior information to be specified. A three-pool model was used incorporating water protons, amide protons, and magnetization transfer effect. The method is demonstrated in simulations, creatine phantoms with varying pH and in vivo (n = 7). The Bayesian model-based approach was able to quantify the APT effect accurately (root-mean-square error < 2%) even when subject to confounding field variation and magnetization transfer effect, unlike traditional asymmetry analysis. The in vivo results gave approximate APT concentration (relative to water) and exchange rate values of 3 × 10(-3) and 15 s(-1) . A degree of correlation was observed between these parameter making the latter difficult to quantify with absolute accuracy, suggesting that more optimal sampling strategies might be required.

Authors: Chappell, M; Donahue, M; Tee, Y; Khrapitchev, A; Sibson, N

• Publication status: Published
• Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine
• Volume: 70
• Issue: 2
• Pages: 556-567
• Publication date: 2013-08-01
• DOI: 10.1002/mrm.24474
• EISSN: 1522-2594
• ISSN: 0740-3194
• Language: English
• Keywords: Computer Simulation; Amides; Protons; Young Adult; Bayes Theorem; Image Enhancement; Brain; Humans; Sensitivity and Specificity; Adult; Body Water; Models, Neurological; Image Interpretation, Computer-Assisted; Reproducibility of Results; Pattern Recognition, Automated; Magnetic Resonance Imaging; Male