ITR/AP: Simulation-Based Medical Planning for Cardiovascular Disease

ITR/AP：基于模拟的心血管疾病医疗计划目前治疗先天性和获得性心血管疾病的介入和手术计划完全依赖于诊断成像数据来确定患者的现状，经验数据来评估类似患者先前治疗的有效性，以及外科医生对首选治疗的判断。人类生物系统的个体变异性和内在复杂性是这样的，仅靠诊断成像和经验数据不足以预测特定治疗对个别患者的结果。本提案中描述的具体目标是开发基于模拟的医疗规划的问题解决环境，该环境结合(I)直接从医学成像数据构建特定于患者的人体血管系统的术前几何模型，(Ii)修改这些模型以包含多个潜在的介入和手术计划，(Iii)生成治疗计划的有限元网格，(Iv)模拟这些特定于患者的模型中的血流，以及(V)所产生的生理信息的可视化和量化。将改进使用二维和三维水平集方法从计算机断层扫描(CT)和磁共振成像(MRI)数据识别血管边界的技术，以提高准确性和效率。成功完成上述任务的最终结果将是开发一个集成了图像分割、几何建模、网格生成、计算力学和科学可视化技术的基于模拟的医学规划问题解决环境。

ITR/AP: Simulation-Based Medical Planning for Cardiovascular DiseaseThe current paradigm for interventional and surgery planning for the treatment of congenital and acquired cardiovascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients, and the judgement of the surgeon to decide on a preferred treatment. The individual variability and inherent complexity of human biological systems is such that diagnostic imaging and empirical data alone are insufficient to predict the outcome of a given treatment for an individual patient. The specific objectives described in the present proposal are to develop a Problem Solving Environment for Simulation-Based Medical Planning combining (i) the construction of patient-specific preoperative geometric models of the human vascular system directly from medical imaging data, (ii) the modification of these models to incorporate multiple potential interventional and surgical plans, (iii) the generation of finite element meshes of the treatment plans, (iv) the simulation of blood flow in these patient-specific models, and (v) the visualization and quantification of resulting physiologic information. Techniques for identifying vessel boundaries from computed tomography (CT) and magnetic resonance imaging (MRI) data using two- and three-dimensional level set methods will be improved to enhance accuracy and efficiency. The ultimate result of the successful completion of the outlined tasks will be the development of an integrated Problem Solving Environment for Simulation-Based Medical Planning incorporating image segmentation, geometric modeling, mesh generation, computational mechanics, and scientific visualization techniques.