Medical imaging derived cardiac biomechanical models offer a wealth of new information to be used in diagnosis and prognosis of cardiovascular disease. A noteworthy feature of such models is the ability to predict myofiber contraction stresses during acute or chronic ischemic events. Current techniques for heterogeneous contraction models require tissue motion tracking capabilities which are neither available on all imaging modalities, nor currently used in the clinic. Proposed in this article is a proof of concept of a tissue tracking independent technique focused on shape optimization to predict the contraction stresses of in-silico left ventricle models simulating various acute myocardial infarction events. The technique involves three variables defined in the left ventricle muscle. Two of the variables represent the contraction stresses in the healthy and infarct regions while the third is a novel periinfarct variable defining a non-contracting myofiber state allowing finer classification of local myofiber damage. Results indicate that the contraction stress reconstruction errors are overall smaller than 12% when considering standard errors associated with population modelling for the new variable of interest.

医学成像衍生的心脏生物力学模型提供了许多新信息，可用于心血管疾病的诊断和预后。这种模型的一个值得注意的特征是能够预测急性或慢性缺血事件中的肌纤维收缩压力。用于异构收缩模型的当前技术需要组织运动跟踪功能，该功能既不能在所有成像方式上使用，也不能在临床上使用。本文提出了一种组织跟踪独立技术的概念证明，该技术专注于形状优化以预测模拟各种急性心肌梗塞事件的硅左心室模型的收缩应力。该技术涉及在左心室肌中定义的三个变量。其中两个变量代表健康和梗塞区域的收缩压力，而第三个变量是新的梗死周围变量，定义了非收缩性肌纤维状态，可以对局部肌纤维损伤进行更好的分类。结果表明，当针对与目标变量有关的新变量考虑标准误差时，收缩应力重建误差总体上小于12％。