论文标题
具有替代模型的动脉组织机械模型的逆不确定性定量
Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling
论文作者
论文摘要
冠状动脉疾病导致严重的健康问题，例如动脉粥样硬化，心绞痛，心脏病发作甚至死亡。考虑到冠状动脉动脉的临床意义，有效的计算模型是迈向组织工程，增强冠状动脉疾病研究并开发医疗治疗和介入工具的至关重要的一步。在这项工作中，我们将逆不确定性定量应用于基于微观的动脉组织模型，这是多尺度内再狭窄模型的组成部分。进行了逆不确定性定量以校准动脉组织模型，以与组织实验数据一致地实现机械响应。使用偏置项校正进行贝叶斯校准，以减少吸引力功能的未知多项式系数的不确定性，并根据单轴菌株测试与动脉组织的机械行为一致。由于模型的计算成本很高，因此开发了基于高斯工艺的替代模型，以确保计算的可行性。
Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computational model is a vital step towards tissue engineering, enhancing the research of coronary diseases and developing medical treatment and interventional tools. In this work, we applied inverse uncertainty quantification to a microscale agent-based arterial tissue model, a component of a multiscale in-stent restenosis model. Inverse uncertainty quantification was performed to calibrate the arterial tissue model to achieve the mechanical response in line with tissue experimental data. Bayesian calibration with bias term correction was applied to reduce the uncertainty of unknown polynomial coefficients of the attractive force function and achieved agreement with the mechanical behaviour of arterial tissue based on the uniaxial strain tests. Due to the high computational costs of the model, a surrogate model based on Gaussian process was developed to ensure the feasibility of the computation.
