Menon Karthik, Zanoni Andrea, Khan M Owais, Geraci Gianluca, Nieman Koen, Schiavazzi Daniele E, Marsden Alison L
Woodruff School of Mechanical Engineering and Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA, USA; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
Comput Methods Programs Biomed. 2025 Nov;271:108951. doi: 10.1016/j.cmpb.2025.108951. Epub 2025 Jul 19.
Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline.
We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data.
We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs.
Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance.
The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.
与仅依靠解剖成像相比，冠状动脉血流动力学的非侵入性模拟改善了冠状动脉疾病的临床风险分层和治疗结果。然而，模拟通常采用经验方法在冠状动脉树的各动脉之间分配总冠状动脉血流，这忽略了患者的个体差异、疾病的存在以及其他临床因素。此外，临床数据中的不确定性在建模流程中往往未得到考虑。
我们提出了一种端到端的不确定性感知流程，以（1）通过纳入特定血管的冠状动脉血流以及心功能来实现冠状动脉血流模拟的个性化；（2）在考虑临床数据不确定性的同时，更精确地预测感兴趣的临床和生物力学量。
我们将来自临床CT心肌灌注成像的患者特异性心肌血流测量值进行同化，以估计特定分支的冠状动脉血流。利用临床数据中的模拟噪声，通过自适应马尔可夫链蒙特卡罗采样来估计模型参数的联合后验分布。此外，使用一种将多保真度蒙特卡罗估计与非线性、数据驱动的降维相结合的新方法来确定感兴趣的相关量的后验预测分布。这导致了高保真度和低保真度模型输出之间的相关性得到改善。
我们的框架在测量噪声不确定性下准确地再现了临床测量的心功能以及特定分支的冠状动脉血流。与单保真度蒙特卡罗估计和最先进的多保真度蒙特卡罗方法相比，我们观察到感兴趣的估计量的置信区间大幅缩小。对于低保真度和高保真度模型预测之间相关性有限的感兴趣量，尤其如此。此外，在指定的置信水平或方差下，所提出的多保真度蒙特卡罗估计器的计算成本比传统估计器显著更低。
所提出的用于冠状动脉血流动力学个性化和不确定性感知预测的流程基于常规临床测量以及最近开发的CT心肌灌注成像技术。所提出的流程在精度上有显著提高，计算成本降低。
