Hierarchical Bayesian myocardial perfusion quantification

Cian M. Scannell (Corresponding author), Amedeo Chiribiri, Adriana D.M. Villa, Marcel Breeuwer, Jack Lee

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Original languageEnglish
Article number101611
Number of pages12
JournalMedical Image Analysis
Volume60
Early online date9 Nov 2019
DOIs
Publication statusE-pub ahead of print - 9 Nov 2019

Fingerprint

Perfusion
Computer Simulation
Magnetic Resonance Spectroscopy
Signal-To-Noise Ratio
Magnetic resonance
Least-Squares Analysis
Artifacts
Blood
Imaging techniques
Kinetics
Health
Kinetic parameters
Signal to noise ratio

Bibliographical note

Copyright © 2019. Published by Elsevier B.V.

Cite this

Scannell, Cian M. ; Chiribiri, Amedeo ; Villa, Adriana D.M. ; Breeuwer, Marcel ; Lee, Jack. / Hierarchical Bayesian myocardial perfusion quantification. In: Medical Image Analysis. 2020 ; Vol. 60.
@article{78fdf49bad694104857f46952d5e2147,
title = "Hierarchical Bayesian myocardial perfusion quantification",
abstract = "Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.",
author = "Scannell, {Cian M.} and Amedeo Chiribiri and Villa, {Adriana D.M.} and Marcel Breeuwer and Jack Lee",
note = "Copyright {\circledC} 2019. Published by Elsevier B.V.",
year = "2019",
month = "11",
day = "9",
doi = "10.1016/j.media.2019.101611",
language = "English",
volume = "60",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",

}

Hierarchical Bayesian myocardial perfusion quantification. / Scannell, Cian M. (Corresponding author); Chiribiri, Amedeo; Villa, Adriana D.M.; Breeuwer, Marcel; Lee, Jack.

In: Medical Image Analysis, Vol. 60, 101611, 02.2020.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Hierarchical Bayesian myocardial perfusion quantification

AU - Scannell, Cian M.

AU - Chiribiri, Amedeo

AU - Villa, Adriana D.M.

AU - Breeuwer, Marcel

AU - Lee, Jack

N1 - Copyright © 2019. Published by Elsevier B.V.

PY - 2019/11/9

Y1 - 2019/11/9

N2 - Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

AB - Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

U2 - 10.1016/j.media.2019.101611

DO - 10.1016/j.media.2019.101611

M3 - Article

C2 - 31760191

VL - 60

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

M1 - 101611

ER -