Bayesian Analysis

A Bayesian image analysis of radiation induced changes in tumor vascular permeability

Yue Cao, Timothy D. Johnson, Roderick J. A. Little, and Xiaoxi Zhang

Full-text: Open access

Abstract

This work is motivated by a quantitative Magnetic Resonance Imaging study of the relative change in tumor vascular permeability during the course of radiation therapy. The differences in tumor and healthy brain tissue physiology and pathology constitute a notable feature of the image data---spatial heterogeneity with respect to its contrast uptake profile (a surrogate for permeability) and radiation induced changes in this profile. To account for these spatial aspects of the data, we employ a Gaussian hidden Markov random field (MRF) model. The model incorporates a latent set of discrete labels from the MRF governed by a spatial regularization parameter. We estimate the MRF regularization parameter and treat the number of MRF states as a random variable and estimate it via a reversible jump Markov chain Monte Carlo algorithm. We conduct simulation studies to examine the performance of the model and compare it with a recently proposed method using the Expectation-Maximization (EM) algorithm. Simulation results show that the Bayesian algorithm performs as well, if not slightly better than the EM based algorithm. Results on real data suggest that the tumor "core" vascular permeability increases relative to healthy tissue three weeks after starting radiotherapy, which may be an opportune time to initiate chemotherapy and warrants further investigation.

Article information

Source
Bayesian Anal., Volume 5, Number 1 (2010), 189-212.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340369798

Digital Object Identifier
doi:10.1214/10-BA508

Mathematical Reviews number (MathSciNet)
MR2596441

Zentralblatt MATH identifier
1330.62267

Keywords
Hidden Markov random fields Mann Whitney U statistic Quantitative Magnetic Resonance Imaging Reversible jump MCMC Swendsen-Wang algorithm Image Analysis Quantitative MRI

Citation

Zhang, Xiaoxi; Johnson, Timothy D.; Little, Roderick J. A.; Cao, Yue. A Bayesian image analysis of radiation induced changes in tumor vascular permeability. Bayesian Anal. 5 (2010), no. 1, 189--212. doi:10.1214/10-BA508. https://projecteuclid.org/euclid.ba/1340369798


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