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.

在过去的一个世纪中，心血管手术的进步主要是通过“试验和错误”方法来实现的，使用外科医生经验以及对患者结局的评估以判断成功。另一方面，工程领域为计算模拟和优化开发了复杂的工具，这些工具现在已在设计过程中变得司空见惯。 类似的工具可以通过提供系统地测试新的手术设计的手段对患者的风险并为个别患者定制设计，从而极大地使医疗领域受益。 尽管在开发心血管模拟方法方面取得了长足的进步，但在订购患者特定模拟之前，障碍仍然存在，就像订购胸部X射线一样容易。 在诊所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范围的网络基础设施，以及缺乏用于手术设计的有效操纵和优化工具的工具。该项目的主要目的是先开发新颖和有效的计算方法，以便在计算和设备设计方面使用这些工具的开发，并使用这些工具进行了效果，并使用这些工具来使用这些工具，并使用这些设备设计来实现这些功能。 将开发新的网络基础结构，包括用于患者特定几何参数化的新的基于物理的工具，并扩展了在平行环境中使用的优化和不确定性量化方法。 这种优化和不确定性框架将导致多层并联计算结构，其中将同时执行多个成本函数评估，每个评估都需要多处理器有限元元素模拟。 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程序。 特别是，PI将应用计算方法和工具来（1）对手术的设计进行定制，以治疗单个心室心脏缺陷的儿童，（2）量化由川崎疾病引起的冠状动脉瘤中的血液动力学，以及（3）执行鲁棒的设计以改善冠状动脉旁路旁路疗程。 PI还将使用系统的不确定性定量工具来评估心血管模拟的可靠性，以提高对结果的信心。 将来，该框架将用于为患有多种先天性和心脏病的患者设计个体治疗方法。 这些工具有可能影响患者的生活质量，延迟对心脏缺陷儿童的运动耐受性的需求，并在某些情况下降低死亡率。 最佳设计工具的应用将通过提供第一种定量和系统的方法来优化手术和治疗计划，从而带来范式转移到医疗界。 这些工具将在需要优化和大规模数值求解器之间耦合（包括湍流，燃烧，流体结构相互作用和医疗设备设计）之间进行更广泛的使用。 我们将领导一项综合的跨学科教育和外展计划，该计划将吸引高中学生，尤其是妇女和少数民族，进入工程和计算科学领域。 我们的教育计划将通过将学生暴露于心血管医学以及医生使用基于定量模拟的工具中来解决新的跨学科领域的培训需求。外展计划，包括一项课后科学课程和圣地亚哥科学节上的一个摊位，将通过将他们暴露于新兴的研究和职业选择中，吸引弱势学生进入科学和工程。