Schwarz Erica L, Pegolotti Luca, Pfaller Martin R, Marsden Alison L
Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA.
Biophys Rev (Melville). 2023 Mar;4(1):011301. doi: 10.1063/5.0109400. Epub 2023 Jan 13.
Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.
基于物理的心血管系统计算模型越来越多地用于模拟健康和疾病状态演变过程中的血流动力学、组织力学和生理学。虽然使用计算流体动力学（CFD）的预测模型最初主要用于手术规划，但其应用现在已远远超出了这一目的。在本综述中，我们描述了越来越广泛的建模应用，旨在揭示疾病进展和发展的基本机制、进行模型引导设计以及生成可测试的假设以推动有针对性的实验。模型越来越多地纳入跨越心脏和血管系统广泛时间和长度尺度的多个物理过程。随着这些能力的扩展，先天性和后天性心血管疾病中患者特异性建模的临床应用也在增加，影响着复杂先天性心脏病、冠状动脉疾病、血管外科手术、肺动脉疾病和医疗器械设计中的临床护理和治疗决策。为支持这些努力，我们讨论了建模方法的最新进展，当由临床需求驱动时这些进展最具影响力。我们描述了图像处理、流固相互作用、不确定性下建模以及降阶建模方面的关键最新进展，以实现临床相关时间范围内的模拟。在所有这些领域，我们认为仅传统的CFD不足以解决跨尺度和系统日益复杂的临床和生物学问题。相反，CFD应与适当的多尺度生物学、物理和生理学模型相结合，以生成机械生物学系统和复杂临床场景的全面、有影响力的模型。基于这一观点，我们最后概述了该领域的开放问题和未来挑战。
