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TOMOGRAPHY, September 2015, Volume 1, Issue 1: 61-68
DOI: 10.18383/j.tom.2015.00133

Renal DCE-MRI Model Selection Using Bayesian Probability Theory

Scott C. Beeman1, Patrick Osei-Owusu2, Chong Duan3, John Engelbach1, G. Larry Bretthorst1, Joseph J. H. Ackerman1,3,4, Kendall J. Blumer2, and Joel R. Garbow1

1Departments of Radiology, 2Cell Biology and Physiology, 3Chemistry, and 4Medicine, Washington University, St. Louis, MO


The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-en- hanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabili- ties of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak–Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation dem- onstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexpo- nential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valu- able tool for model selection and parameter estimation.


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