每年我国约有350万人死于心血管疾病，其中有90%的人患有室性心律失常。可是，现有的技术和手段难以有效地对心室病变进行准确的早期诊断和治疗。其根本原因在于对个体化心室复杂的生命活动现象缺乏系统性的理论认识。因此本课题在前四项基金项目工作的基础之上，拟开展面向临床的、可预测的、多模态的个体化虚拟生理心室建模与仿真方法研究。基于个体病人的生理功能数据和解剖结构数据，利用图像分割与数据融合、虚拟心脏建模与仿真、并行计算和科学可视化的相关理论与方法，重点研究个体化心室模型的构建方法，心室解剖数据和功能数据（电压、电流、离子浓度、力学形变）融合方法。通过仿真与临床验证来提取心室病变过程中临床上无法观察到的病理指标，以辅助医生做治疗决策；通过仿真来确定心律失常的颤源，来引导医生射频消融；通过药物仿真来筛选最佳治疗药物，以辅助医生用药；从而，为个体化心脏疾病的预防、治疗与药物筛选提供一个有效的研究手段。

Each year in China about 3.5 million people die from cardiovascular diseases, 90% of them suffering from ventricular arrhythmias. However, existing techniques and approaches can hardly do early and accurate diagnosis and treatment on ventricular diseases. The fundamental cause is the lack of a systematic understanding of the complex phenomenon in individual ventricular activities. Therefore, in this project, which is on the basis of our four previous projects, we try to research the clinical, predictable, multi-modal individualized virtual physiological ventricular modeling and simulation methods. Based on the physiological data and the anatomical structure of an individual patient, using image segmentation, data fusion, virtual heart modeling and simulation, parallel computing, and scientific visualization methods, we focus on the construction methods of patient-specific ventricular models and fusion methods of ventricular anatomical data and functional data (voltage, current, ion concentration, mechanical deformation). By simulation and clinical validation, we want to help doctors make treatment decisions by extracting ventricular lesions which cannot be observed during clinicopathological procedures; to guide radiofrequency ablation by determining the fibrillation source of ventricular arrhythmias; to assist medication by screening for the best drugs; to provide an effective research tool for individualized heart disease prevention, treatment and drug screening.