INRIA, Asclepios Research Project, Sophia Antipolis, Nice, France.
Biomech Model Mechanobiol. 2013 Aug;12(4):815-31. doi: 10.1007/s10237-012-0446-z. Epub 2012 Oct 12.
Patient-specific cardiac modelling can help in understanding pathophysiology and predict therapy planning. However, it requires to personalize the model geometry, kinematics, electrophysiology and mechanics. Calibration aims at providing proper initial values of parameters before performing the personalization stage which involves solving an inverse problem. We propose a fast automatic calibration method of the mechanical parameters of a complete electromechanical model of the heart based on a sensitivity analysis and the Unscented Transform algorithm. A new implementation of the complete Bestel-Clement-Sorine (BCS) cardiac model is also proposed, in a modular and efficient framework. A complete sensitivity analysis is performed that reveals which observations on the volume evolution are significant to characterize the global behaviour of the myocardium. We show that the calibration method gives satisfying results by optimizing up to 5 parameters of the BCS model in only one iteration. This method was evaluated synthetically as well as on 7 volunteers with a mean relative error from the real data of 10 %. This calibration is designed to replace manual parameter estimation as well as initialization steps that precede automatic personalization algorithms based on images.
患者特异性心脏建模有助于理解病理生理学并预测治疗计划。然而，它需要对模型的几何形状、运动学、电生理学和力学进行个性化处理。校准旨在在执行个性化阶段之前提供参数的适当初始值，个性化阶段涉及解决反问题。我们提出了一种基于灵敏度分析和无迹变换算法的快速自动校准完整机电心脏模型力学参数的方法。还提出了一种新的完整 Bestel-Clement-Sorine (BCS) 心脏模型的实现，该模型具有模块化和高效的框架。进行了完整的灵敏度分析，揭示了体积演化的哪些观察结果对于表征心肌的整体行为具有重要意义。我们表明，通过仅在一次迭代中优化 BCS 模型的多达 5 个参数，该校准方法可以得到令人满意的结果。该方法在合成数据以及 7 名志愿者上进行了评估，与真实数据的平均相对误差为 10%。该校准旨在替代手动参数估计以及基于图像的自动个性化算法之前的初始化步骤。
