Renal DCE-MRI Model Selection Using Bayesian Probability Theory

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

doi:10.18383/j.tom.2015.00133

Open AccessArticle

Renal DCE-MRI Model Selection Using Bayesian Probability Theory

by

Scott C. Beeman

^1,*,

Patrick Osei-Owusu

²,

Chong Duan

³,

John Engelbach

¹,

G. Larry Bretthorst

¹,

Joseph J. H. Ackerman

^1,3,4,

Kendall J. Blumer

² and

Joel R. Garbow

^1,*

¹

Departments of Radiology, Washington University, St. Louis, MO, USA

²

Departments of Cell Biology and Physiology, Washington University, St. Louis, MO, USA

³

Departments of Chemistry, Washington University, St. Louis, MO, USA

⁴

Departments of Medicine, Washington University, St. Louis, MO, USA

^*

Authors to whom correspondence should be addressed.

Tomography 2015, 1(1), 61-68; https://doi.org/10.18383/j.tom.2015.00133

Submission received: 4 June 2015 / Revised: 8 July 2015 / Accepted: 11 August 2015 / Published: 1 September 2015

Download Versions Notes

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.

Keywords: Bayesian model selection; dynamic contrast enhanced MRI; signal modeling

Share and Cite

MDPI and ACS Style

Beeman, S.C.; Osei-Owusu, P.; Duan, C.; Engelbach, J.; Bretthorst, G.L.; Ackerman, J.J.H.; Blumer, K.J.; Garbow, J.R. Renal DCE-MRI Model Selection Using Bayesian Probability Theory. Tomography 2015, 1, 61-68. https://doi.org/10.18383/j.tom.2015.00133

AMA Style

Beeman SC, Osei-Owusu P, Duan C, Engelbach J, Bretthorst GL, Ackerman JJH, Blumer KJ, Garbow JR. Renal DCE-MRI Model Selection Using Bayesian Probability Theory. Tomography. 2015; 1(1):61-68. https://doi.org/10.18383/j.tom.2015.00133

Chicago/Turabian Style

Beeman, Scott C., Patrick Osei-Owusu, Chong Duan, John Engelbach, G. Larry Bretthorst, Joseph J. H. Ackerman, Kendall J. Blumer, and Joel R. Garbow. 2015. "Renal DCE-MRI Model Selection Using Bayesian Probability Theory" Tomography 1, no. 1: 61-68. https://doi.org/10.18383/j.tom.2015.00133

APA Style

Beeman, S. C., Osei-Owusu, P., Duan, C., Engelbach, J., Bretthorst, G. L., Ackerman, J. J. H., Blumer, K. J., & Garbow, J. R. (2015). Renal DCE-MRI Model Selection Using Bayesian Probability Theory. Tomography, 1(1), 61-68. https://doi.org/10.18383/j.tom.2015.00133

Article Menu

Renal DCE-MRI Model Selection Using Bayesian Probability Theory

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI