AI 中文总结
提出贝叶斯代理建模框架，通过多输出变分高斯过程仿真器推断左心室五个生理区域的Holzapfel-Ogden材料参数，在点精度相当下更优地量化不确定性，并验证了局部与全局硬化场景及健康志愿者数据的可行性。
AI 中文摘要
患者特异性左心室生物力学模型可将心脏磁共振成像与区域心肌材料特性联系起来，但现有的基于仿真器的研究通常将心肌视为力学均匀体，限制了对局部功能障碍的表征。我们提出了一种贝叶斯代理建模框架，用于推断左心室中区域性的Holzapfel-Ogden材料参数，该左心室被划分为基于美国心脏协会17节段模型的五个生理区域。我们筛选了八种仿真器策略，涵盖单输出与多输出、局部与全局、以及基于高斯过程与基于神经网络的架构，并使用参数点估计精度进行评估；保留的三个模型通过经验边际可信区间覆盖率和后验收缩率进行了评估。我们发现，点精度相当的模型在不确定性方面却存在显著差异。多输出变分高斯过程在这些标准中提供了最有利的平衡，并被保留用于后续分析。在合成的局部和全局硬化场景中，最大后验估计通常能区分硬化区域与基线区域，但非线性硬化参数比刚度幅度参数更难从舒张末期观测中识别。一项健康志愿者分析证明了该方法在不完整观测向量和联合推断应变噪声尺度下的可行性。这些结果表明，所提出的框架为区域左心室参数推断提供了一种计算可行且具有不确定性意识的方法。
英文摘要
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.
