CDS&E: Uncertainty Quantification and Bayesian Updating in Data-Driven Cardiovascular Modeling

CDS

• 批准号: 1508794
• 负责人: Alison Marsden
• 金额: $ 37.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1508794&HistoricalAwards=false
• 关键词: CDS& Uncertainty; Quantification; Bayesian; Updating

CBET - 1508794Marsden, Alison L.Cardiovascular disease is one of the major problems facing US and the world. While simulations of cardiovascular hemodynamics are now being used to study fundamental processes, trust in personalized simulation results before making clinical decisions for a patient is absent due to several uncertainties. This is exactly what this proposal is about, investigating these uncertainties and developing techniques to allow informed medical decisions. The co-PIs propose to disseminate their computational tools as open source programs. Though it is well known that cardiovascular simulations require numerous assumptions and assimilation of uncertain clinical data, these uncertainties currently get swept under the rug, asking end-users to accept deterministic simulation predictions as "truth" with no associated statistics. As a result, researchers and clinicians are left to wonder "How reliable are simulation predictions in light of myriad uncertainties?" and "How do the statistics on output predictions change with differing methodologies and assumptions?". These questions lead to justified skepticism in the research and clinical community, and are a roadblock to adoption. Development of transformative technology to assess uncertainty, currently lacking in the field, is of paramount importance for safe and routine adoption of simulations for personalized medicine and biomechanics research. This is the area that this proposal comes to cover, as it aspires to develop techniques that can lead to the incorporation of data-driven cardiovascular models to inform decisions surrounding choices of drug therapy, device placement, surgical methods and interventions for individual patients. The proposal has two goals: 1) Develop fast automated methods for parameter estimation and assimilation of uncertain data into multiscale models, 2) Develop an efficient framework to propagate uncertainties from clinical and imaging data to simulation predictions. It is proposed to demonstrate the uncertainty quantification (UQ) framework through application to multiscale simulations of coronary artery disease (CAD), though the framework will apply to a wide range of other cardiovascular and respiratory diseases. Simulations will be run in a high performance computing (HPC) environment using a multi-level parallel algorithm structure. The ultimate goal is to address currently unanswered questions about reliability and robustness in cardiovascular simulation. Results from this work, if successful, would enable acceptance of computational models and establish reliability metrics to guide model improvement and data collection. Cardiovascular simulations have potential to personalize treatments for individual patients and to characterize the in vivo mechanical environment, providing key biomechanical data that cannot be readily obtained from medical imaging. The proposed computational framework could be applicable to a range of problems in biomedical computing, biological modeling, and engineering applications using computational fluid dynamics. Dissemination will be achieved through contributions to the SimVascular open source project, for which Dr. Marsden is the PI. Activities that integrate research and teaching by introducing statistics concepts in graduate level courses and through outreach to middle and high school students are proposed.

CBET -1508794 Marsden，Alison L.心血管疾病是美国和世界面临的主要问题之一。虽然心血管血液动力学的模拟现在被用于研究基本过程，但由于几个不确定性，在为患者做出临床决策之前，对个性化模拟结果的信任是不存在的。这正是这项提案的目的，调查这些不确定性并开发技术以做出明智的医疗决定。co-PI建议将他们的计算工具作为开源程序进行传播。虽然众所周知，心血管模拟需要大量的假设和不确定的临床数据的同化，但这些不确定性目前被扫到地毯下，要求最终用户接受确定性模拟预测作为“真理”，没有相关的统计数据。因此，研究人员和临床医生都想知道“鉴于无数的不确定性，模拟预测的可靠性如何？以及“产出预测的统计数据如何随不同的方法和假设而变化？".这些问题导致了研究和临床社区的合理怀疑，并且是采用的障碍。开发变革性技术来评估目前该领域缺乏的不确定性，对于安全和常规地采用个性化医学和生物力学研究的模拟至关重要。这是该提案即将涵盖的领域，因为它希望开发能够导致数据驱动的心血管模型整合的技术，以告知围绕药物治疗，设备放置，手术方法和个体患者干预措施选择的决策。该提案有两个目标：1）开发快速自动化方法，用于参数估计和将不确定数据同化到多尺度模型中，2）开发有效的框架，将不确定性从临床和成像数据传播到模拟预测。建议通过应用于冠状动脉疾病（CAD）的多尺度模拟来证明不确定性量化（UQ）框架，尽管该框架将适用于广泛的其他心血管和呼吸系统疾病。模拟将在高性能计算（HPC）环境中使用多级并行算法结构运行。最终目标是解决目前尚未回答的问题，在心血管模拟的可靠性和鲁棒性。这项工作的结果，如果成功的话，将能够接受计算模型，并建立可靠性指标，以指导模型的改进和数据收集。心血管模拟有可能为个体患者提供个性化治疗，并表征体内力学环境，提供无法从医学成像中获得的关键生物力学数据。所提出的计算框架可以适用于生物医学计算，生物建模和工程应用中的一系列问题，使用计算流体动力学。传播将通过对SimVascular开源项目的贡献来实现，Marsden博士是该项目的PI。通过在研究生课程中引入统计概念并通过向初中和高中学生推广，将研究和教学结合起来的活动被提出。