Problem
Research questions and friction points this paper is trying to address.
Bayesian inference
left ventricle
material parameters
regional myocardial properties
emulation strategies
Emulation strategies for Bayesian inference of regional left ventricle material parameters
🤖 AI Summary
研究提出了一种贝叶斯代理建模框架，通过比较多种模拟策略，使用多输出变分高斯过程方法来推断左心室区域材料参数，解决了现有研究中将心肌视为机械同质的局限性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian surrogate-modelling
multi-output variational Gaussian process
regional Holzapfel-Ogden material parameters
uncertainty-aware approach
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