CAREER: Optimization and Parameterization for Multiscale Cardiovascular Flow Simulations Using High Performance Computing

职业：使用高性能计算进行多尺度心血管血流模拟的优化和参数化

• 批准号: 1150184
• 负责人: Alison Marsden
• 金额: $ 42.76万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1150184&HistoricalAwards=false
• 关键词: CAREER; Optimization; Parameterization; Multiscale; Cardiovascular

For the past century, advances in cardiovascular surgery have mainly come about through a `trial and error' approach, using surgeon experience, and evaluation of patient outcomes to judge success. On the other hand, the engineering field has developed sophisticated tools for computational simulation and optimization that have now become commonplace in the the design process. Similar tools could greatly benefit the medical field by offering the means to systematically test new surgical designs at no risk to the patient, and to customize designs for individual patients. While great strides have been made in developing cardiovascular simulation methods, hurdles remain before ordering a patient-specific simulation is as easy as, for example, ordering a chest x-ray. Major roadblocks to adoption of these methods in the clinic include the current lack of cyberinfrastructure that can achieve clinically relevant time frames, as well as a lack of tools for efficient manipulation and optimization of surgical designs.The main objective of this project is to pioneer the development of novel and efficient computational methods which can be applied for optimization in surgery and device design, and to demonstrate the use of these tools using high performance computing. Novel cyberinfrastructure will be developed, including new physics-based tools for patient specific geometry parameterization, and expanded optimization and uncertainty quantification methods for use in a parallel environment. This optimization and uncertainty framework will result in a multi-layered parallel computing structure, in which multiple cost function evaluations will be performed simultaneously, each requiring a multi-processor finite element simulation. These unique computational approaches will be applied to three cardiovascular shape optimization applications using high performance computing. In particular, the PI will apply the computational methods and tools to (1) perform customization of designs for surgery to treat children with single ventricle heart defects, (2) quantify hemodynamics in coronary aneurysms caused by Kawasaki disease, and (3) perform robust design to improve coronary artery bypass graft surgery. The PI will also use systematic uncertainty quantification tools to assess the reliability of cardiovascular simulations to improve confidence in results. In the future, this framework will be used to design individual treatments for patients suffering from a wide range of congenital and acquired heart diseases. These tools have potential to impact quality of life for patients, delay the need for a heart transplant, increase exercise tolerance for children with heart defects, and in some cases reduce mortality. The application of optimal design tools will bring a paradigm shift to the medical community by offering the first quantitative and systematic methods for optimizing surgeries and treatment plans at no risk to the patient. These tools will have broader use in a range of engineering applications requiring coupling between optimization and large scale numerical solvers, including turbulence, combustion, fluid structure interaction, and medical device design. We will lead an integrated interdisciplinary education and outreach plan that will draw high school students, particularly women and minorities, to the field of engineering and computational science. Our education plan will address training needs in a new interdisciplinary area by exposing students to cardiovascular medicine, and doctors to quantitative simulation-based tools. The outreach program, including an after school science program and a booth at the San Diego Science Festival, will draw disadvantaged students to science and engineering by exposing them to emerging research and career options.

在过去的一个世纪里，心血管外科的进步主要是通过“反复试验”的方法取得的，利用外科医生的经验和对患者结果的评估来判断成功与否。另一方面，工程领域已经开发出用于计算模拟和优化的复杂工具，这些工具现已在设计过程中变得司空见惯。 类似的工具可以在不对患者造成风险的情况下系统地测试新的手术设计，并为个体患者定制设计，从而极大地造福医疗领域。 尽管在开发心血管模拟方法方面已经取得了长足的进步，但在订购针对特定患者的模拟像订购胸部 X 光检查一样简单之前，仍然存在障碍。 在临床上采用这些方法的主要障碍包括目前缺乏可以实现临床相关时间框架的网络基础设施，以及缺乏有效操作和优化手术设计的工具。该项目的主要目标是开创开发新颖且高效的计算方法，可应用于手术和设备设计的优化，并通过高性能计算演示这些工具的使用。 将开发新型网络基础设施，包括用于患者特定几何参数化的新的基于物理的工具，以及在并行环境中使用的扩展优化和不确定性量化方法。 这种优化和不确定性框架将产生多层并行计算结构，其中多个成本函数评估将同时执行，每个评估都需要多处理器有限元模拟。 这些独特的计算方法将应用于使用高性能计算的三种心血管形状优化应用。 特别是，PI 将应用计算方法和工具来 (1) 进行手术设计的定制，以治疗患有单心室心脏缺陷的儿童，(2) 量化川崎病引起的冠状动脉瘤的血流动力学，以及 (3) 执行稳健的计算旨在改善冠状动脉搭桥手术的设计。 PI 还将使用系统不确定性量化工具来评估心血管模拟的可靠性，以提高结果的可信度。 未来，该框架将用于为患有各种先天性和后天性心脏病的患者设计个体化治疗方法。 这些工具有可能影响患者的生活质量，推迟心脏移植的需要，提高患有心脏缺陷的儿童的运动耐量，并在某些情况下降低死亡率。 优化设计工具的应用将为医学界带来范式转变，提供第一个定量和系统的方法来优化手术和治疗计划，而不会给患者带来风险。 这些工具将在需要优化和大规模数值求解器之间耦合的一系列工程应用中具有更广泛的用途，包括湍流、燃烧、流体结构相互作用和医疗设备设计。 我们将领导一项综合的跨学科教育和推广计划，吸引高中生，特别是女性和少数族裔，进入工程和计算科学领域。 我们的教育计划将通过让学生接触心血管医学、让医生接触基于定量模拟的工具来满足新的跨学科领域的培训需求。该推广计划包括课后科学计划和圣地亚哥科学节的展位，将通过让弱势学生接触新兴研究和职业选择来吸引他们接触科学和工程。