Renal DCE-MRI Model Selection Using Bayesian Probability Theory.

Scott C Beeman,Joseph J H Ackerman,Chong Duan,Joel R Garbow,G Larry Bretthorst,Kendall J Blumer,John Engelbach,Patrick Osei-Owusu

doi:10.18383/j.tom.2015.00133

Abstract

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-enhanced 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 probabilities 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 demonstrated 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/biexponential 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 valuable tool for model selection and parameter estimation.

Highlights

Renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful technique that can noninvasively quantify and map empirical and physiological parameters that provide information on renal function
Joint Bayesian model selection/parameter estimation demonstrated 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/biexponential model parameter values clearly distinguished cohorts of low and high renal blood flow (RBF) from one another
DCE-MRI can quantify and map renal blood flow (RBF) and the glomerular filtration rate (GFR) [1,2,3,4,5,6], important clinical determinants of renal function that are otherwise traditionally measured based on filtering para-aminohippuric acid and inulin into the urine

Summary

Introduction

Renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful technique that can noninvasively quantify and map empirical and physiological parameters that provide information on renal function. Renal DCE-MRI involves serial imaging of the kidney using a T1-weighted MRI sequence to observe the passage of a bolus of gadolinium-containing contrast agent (CA) through the kidney. From these data, dynamic parameters can be quantified by fitting descriptive mathematical models—those that provide an approximate representation of a complex system—to the MRI data. Pharmacokinetic compartmental models are derived based on approximations of the known physiological processes that underlie the MRI signal. Empirical models can be used to characterize DCE-MRI data using simple, logically chosen math-

Objectives

Methods

Results

Conclusion