Uncertainty Quantification of Fiber Orientation and Epicardial Activation

Predictive models and simulations of cardiac function require accurate representations of anatomy, often to the scale of local myocardial fiber structure. However, acquiring this information in a patient-specific manner is challenging. Moreover, the impact of physiological variability in fiber orientation on simulations of cardiac activation is poorly understood. To explore these effects, we implemented bi-ventricular activation simulations using rule-based fiber algorithms and robust uncertainty quantification techniques to generate detailed maps of model variability. Specifically, we utilized polynomial chaos expansion, enabling efficient exploration with reduced computational demand through an emulator function approximating the underlying forward model. Our study focused on examining the epicardial activation sequences of the heart in response to six stimuli locations and five metrics of activation. Our findings revealed that physiological variability in fiber orientation does not significantly affect the location of activation features, but it does impact the overall spread of activation. We observed low variability near the earliest activation sites, but high variability across the rest of the epicardial surface. We conclude that the level of accuracy of myocardial fiber orientation required for simulation depends on the specific goals of the model and the related research or clinical goals.

心脏功能的预测模型和模拟需要准确的解剖结构呈现，通常要精确到局部心肌纤维结构的尺度。然而，以患者特异性的方式获取这些信息具有挑战性。此外，纤维取向的生理变异性对心脏激动模拟的影响知之甚少。为了探究这些影响，我们使用基于规则的纤维算法和稳健的不确定性量化技术进行了双心室激动模拟，以生成模型变异性的详细图谱。具体而言，我们利用多项式混沌展开，通过一个近似基础正向模型的模拟器函数，在降低计算需求的情况下实现了高效探究。我们的研究重点是检查心脏在心外膜对六个刺激位置和五个激动指标的激动序列。我们的研究结果表明，纤维取向的生理变异性不会显著影响激动特征的位置，但会影响激动的整体传播。我们观察到在最早激动部位附近变异性较低，但在心外膜表面的其他区域变异性较高。我们得出结论，模拟所需的心肌纤维取向的准确性水平取决于模型的特定目标以及相关的研究或临床目标。