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Renal DCE-MRI Model Selection Using Bayesian Probability Theory.

Scott C Beeman¹, Patrick Osei-Owusu², Chong Duan³

¹Departments of Radiology.

Tomography (Ann Arbor, Mich.)

|July 26, 2018

Summary

This summary is machine-generated.

Bayesian model selection effectively identified the best mathematical model for analyzing renal dynamic contrast-enhanced MRI data. This approach accurately distinguished varying levels of renal blood flow in mice.

Keywords:

Bayesian model selection dynamic contrast enhanced MRI signal modeling

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Area of Science:

Biomedical Imaging
Mathematical Modeling
Renal Physiology

Background:

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is crucial for assessing renal function.
Accurate mathematical modeling is essential for interpreting DCE-MRI data.
Selecting the optimal model for renal DCE-MRI analysis remains a challenge.

Purpose of the Study:

To evaluate Bayesian probability theory-based model selection for identifying the best mathematical model for renal DCE-MRI data.
To compare four distinct mathematical models of renal DCE-MRI.

Main Methods:

Collected DCE-MRI data from 21 mice with high, low, or normal renal blood flow (RBF).
Employed Bayesian methods for model parameter estimation and posterior probability calculation.
Investigated four models: empirical monoexponential, empirical biexponential, Patlak-Rutland, and 2-compartment kidney models.

Main Results:

The empirical biexponential model was strongly favored across all RBF cohorts.
Distinct DCE signal characteristics were identified for each RBF cohort.
Individual model parameters effectively differentiated between low and high RBF groups.

Conclusions:

Bayesian model selection provides a robust method for choosing optimal mathematical models in DCE-MRI analysis.
The empirical biexponential model is highly suitable for renal DCE-MRI data.
This Bayesian approach offers a versatile tool for quantitative analysis and can be extended to other research areas.