Jung Alexander, Gsell Matthias A F, Augustin Christoph M, Plank Gernot
Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging-Division of Biophysics, Medical University Graz, 8010 Graz, Austria.
NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria.
Mathematics (Basel). 2022 Mar 4;10(5):823. doi: 10.3390/math10050823. eCollection 2022 Mar 1.
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations-a computational effort compatible with clinical model applications.
被称为心脏数字孪生的个性化心脏功能计算机模型，有望在心血管疾病的临床精准治疗中发挥重要作用。阻碍临床转化的一个主要障碍是，生物物理详细力学模型的个性化涉及巨大的计算成本，这种模型需要识别高维参数向量。在机电（EM）模型中需要识别的一个重要方面是控制心脏收缩和舒张的主动力学参数。在本研究中，我们提出了一种新颖、全自动且高效的方法，用于使用两步多保真度解决方案对生物物理详细的主动力学模型进行个性化。第一步，给定3D EM模型中的主动力学行为由一个纯粹现象学的低保真模型表示，该模型通过根据临床腔室压力数据进行校准在器官尺度上进行个性化。然后，在第二步中，生成节点细胞主动应力、细胞内钙浓度和纤维拉伸的中位数轨迹，并利用这些轨迹使用心脏EM的0D模型在细胞尺度上对所需的高保真模型进行个性化。我们的新方法在由接受主动脉缩窄（CoA）治疗的患者创建的七个人类左心室（LV）EM模型队列上进行了测试。评估了拟合优度、计算成本以及算法对临床数据不确定性和初始猜测变化的鲁棒性。我们证明，我们的多保真度方法仅通过少数（2至4个）昂贵的3D器官尺度模拟就能促进生物物理详细主动应力模型的个性化，这一计算工作量与临床模型应用兼容。
