Biofluid, Tissue and Solid Mechanics for Medical Applications Lab (IBiTech, bioMMeda), Ghent University, Ghent, Belgium.
Department of Surgery, University of California at San Francisco, San Francisco, CA, USA.
Int J Numer Method Biomed Eng. 2019 Jan;35(1):e3151. doi: 10.1002/cnm.3151. Epub 2018 Oct 7.
Computational cardiac mechanical models, individualized to the patient, have the potential to elucidate the fundamentals of cardiac (patho-)physiology, enable non-invasive quantification of clinically significant metrics (eg, stiffness, active contraction, work), and anticipate the potential efficacy of therapeutic cardiovascular intervention. In a clinical setting, however, the available imaging resolution is often limited, which limits cardiac models to focus on the ventricles, without including the atria, valves, and proximal arteries and veins. In such models, the absence of surrounding structures needs to be accounted for by imposing realistic kinematic boundary conditions, which, for prognostic purposes, are preferably generic and thus non-image derived. Unfortunately, the literature on cardiac models shows no consistent approach to kinematically constrain the myocardium. The impact of different approaches (eg, fully constrained base, constrained epi-ring) on the predictive capacity of cardiac mechanical models has not been thoroughly studied. For that reason, this study first gives an overview of current approaches to kinematically constrain (bi) ventricular models. Next, we developed a patient-specific in silico biventricular model that compares well with literature and in vivo recorded strains. Alternative constraints were introduced to assess the influence of commonly used mechanical boundary conditions on both the predicted global functional behavior of the in-silico heart (cavity volumes, stroke volume, ejection fraction) and local strain distributions. Meaningful differences in global functioning were found between different kinematic anchoring strategies, which brought forward the importance of selecting appropriate boundary conditions for biventricular models that, in the near future, may inform clinical intervention. However, whilst statistically significant differences were also found in local strain distributions, these differences were minor and mostly confined to the region close to the applied boundary conditions.
计算心脏力学模型，针对患者个体化，具有阐明心脏（病理）生理学基础、实现对临床重要指标（如僵硬度、主动收缩、做功）的无创定量评估以及预测心血管治疗干预效果的潜力。然而，在临床环境中，可用的成像分辨率往往有限，这限制了心脏模型只能关注心室，而不包括心房、瓣膜以及近端动静脉。在这些模型中，需要通过施加现实的运动学边界条件来考虑周围结构的缺失，这些边界条件最好是通用的，因此不是来自图像。不幸的是，心脏模型的文献中没有一致的方法来对心肌进行运动学约束。不同方法（例如，完全约束基底、约束心外膜环）对心脏力学模型预测能力的影响尚未得到充分研究。因此，本研究首先概述了当前对（双）心室模型进行运动学约束的方法。接下来，我们开发了一种患者特异性的心脏双室模型，与文献和体内记录的应变数据吻合良好。引入了替代约束条件来评估常用力学边界条件对计算心脏全局功能行为（腔室容积、搏出量、射血分数）和局部应变分布的影响。不同运动学锚定策略之间的全局功能存在有意义的差异，这突出了为双室模型选择适当边界条件的重要性，这些边界条件可能会在不久的将来为临床干预提供信息。然而，虽然在局部应变分布中也发现了统计学上的显著差异，但这些差异较小，主要局限于应用边界条件附近的区域。
