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June 2008 Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation
Xiaoxi Zhang, Timothy D. Johnson, Roderick J. A. Little, Yue Cao
Ann. Appl. Stat. 2(2): 736-755 (June 2008). DOI: 10.1214/07-AOAS157


Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov random field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation–Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.


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Xiaoxi Zhang. Timothy D. Johnson. Roderick J. A. Little. Yue Cao. "Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation." Ann. Appl. Stat. 2 (2) 736 - 755, June 2008.


Published: June 2008
First available in Project Euclid: 3 July 2008

zbMATH: 05591296
MathSciNet: MR2524354
Digital Object Identifier: 10.1214/07-AOAS157

Keywords: EM algorithm , Hidden Markov random field , missing data , Model selection , quantitative MRI , stochastic variation

Rights: Copyright © 2008 Institute of Mathematical Statistics


Vol.2 • No. 2 • June 2008
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