Emulation strategies for Bayesian inference of regional left ventricle material parameters
Abstract
Patient-specific biomechanical models of the left ventricle can relate cardiac magnetic resonance imaging to regional myocardial material properties, but existing emulator-based studies typically treat the myocardium as mechanically homogeneous, limiting representation of localised dysfunction. We propose a Bayesian surrogate-modelling framework for inferring regional Holzapfel-Ogden material parameters in a left ventricle partitioned into five physiological zones derived from the American Heart Association 17-segment model. Eight emulator strategies spanning single- versus multi-output, local versus global, and Gaussian-process- versus neural-network-based architectures were screened using parameter point-estimation accuracy; the three retained models were evaluated using empirical marginal credible-interval coverage and posterior contraction. We found that models with comparable point accuracy nevertheless differed markedly in uncertainty. A multi-output variational Gaussian process provided the most favourable balance across these criteria and was retained for the subsequent analyses. In synthetic local and global stiffening scenarios, maximum a posteriori estimates generally distinguished stiffened from baseline zones, but the nonlinear-stiffening parameters were more difficult to identify from end-diastolic observations than the stiffness-magnitude parameters. A healthy-volunteer analysis demonstrates feasibility with an incomplete observation vector and jointly inferred strain-noise scales. These results suggest that the proposed framework provides a computationally feasible, uncertainty-aware approach to regional left ventricle parameter inference.
Related
- Variational Bayesian Inference Based Progressive Multi-Target MIMO Sensing with Nuisance Parameters
- Bayesian Treed Vecchia Gaussian Process Emulation via Sequential Monte Carlo
- Bayesian Emulation of Multi-fidelity Earth System Modelling Using Hierarchical Gaussian Processes
- Efficient Bayesian inference for multiple network data
- Bayesian inference under model misspecification
BibTeX
@misc{ren2026emulation,
title = {{Emulation strategies for Bayesian inference of regional left ventricle material parameters}},
author = {Hongjin Ren and Vinny Davies and Hao Gao and Mu Niu and Benn Macdonald},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20372},
eprint = {2609.20372},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20372},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Emulation strategies for Bayesian inference of regional left ventricle material parameters
AU - Hongjin Ren
AU - Vinny Davies
AU - Hao Gao
AU - Mu Niu
AU - Benn Macdonald
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.20372
UR - https://arxiv.org/abs/2609.20372
AB - Patient-specific biomechanical models of the left ventricle can relate cardiac magnetic resonance imaging to regional myocardial material properties, but existing emulator-based studies typically treat the myocardium as mechanically homogeneous, limiting representation of localised dysfunction. We propose a Bayesian surrogate-modelling framework for inferring regional Holzapfel-Ogden material parameters in a left ventricle partitioned into five physiological zones derived from the American Heart Association 17-segment model. Eight emulator strategies spanning single- versus multi-output, local versus global, and Gaussian-process- versus neural-network-based architectures were screened using parameter point-estimation accuracy; the three retained models were evaluated using empirical marginal credible-interval coverage and posterior contraction. We found that models with comparable point accuracy nevertheless differed markedly in uncertainty. A multi-output variational Gaussian process provided the most favourable balance across these criteria and was retained for the subsequent analyses. In synthetic local and global stiffening scenarios, maximum a posteriori estimates generally distinguished stiffened from baseline zones, but the nonlinear-stiffening parameters were more difficult to identify from end-diastolic observations than the stiffness-magnitude parameters. A healthy-volunteer analysis demonstrates feasibility with an incomplete observation vector and jointly inferred strain-noise scales. These results suggest that the proposed framework provides a computationally feasible, uncertainty-aware approach to regional left ventricle parameter inference.
ER -