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Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

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Hierarchical Bayesian myocardial perfusion quantification.

Cian M Scannell¹, Amedeo Chiribiri², Adriana D M Villa²

¹School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; The Alan Turing Institute London, United Kingdom.

Medical Image Analysis

|November 25, 2019

Summary

This summary is machine-generated.

A new Bayesian inference method improves the accuracy of myocardial blood flow quantification using dynamic contrast-enhanced magnetic resonance (MR) imaging. This approach enhances parameter estimation for cardiac perfusion assessments.

Keywords:

Bayesian inference Myocardial perfusion MRI Tracer-kinetic modelling

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In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

Published on: February 16, 2016

Area of Science:

Cardiovascular Imaging
Medical Physics
Biomedical Engineering

Background:

Myocardial blood flow (MBF) quantification from dynamic contrast-enhanced (DCE) MRI relies on tracer-kinetic models.
Multi-compartment models offer physiological insights but yield unreliable parameter estimates due to data limitations (low SNR, temporal resolution, artifacts).

Purpose of the Study:

To develop and validate a Bayesian inference scheme for reliable parameter estimation in two-compartment exchange models for myocardial perfusion MRI.
To incorporate prior physiological knowledge and spatial correlations to improve model fitting.

Main Methods:

A Bayesian inference framework was developed for tracer-kinetic modeling of myocardial perfusion MRI.
Hierarchical priors were employed to integrate physiological range information and spatial voxel similarity without assuming patient health status.
The method was validated using both in silico simulations and in vivo patient data.

Main Results:

Bayesian inference significantly reduced mean-squared error compared to non-linear least squares fitting in in silico studies.
In vivo, the Bayesian approach yielded parameter values consistent with existing literature.
Generated parameter maps aligned with independent clinical diagnoses for the studied patients.

Conclusions:

The proposed Bayesian inference scheme reliably estimates parameters for myocardial perfusion MRI using two-compartment exchange models.
This method enhances the accuracy and clinical relevance of DCE-MRI-based cardiac perfusion analysis.
Bayesian inference offers a robust alternative to traditional fitting methods for complex kinetic modeling in medical imaging.