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 -1508794Marsden，Alison L.Cardiescular疾病是我们和世界面临的主要问题之一。虽然现在使用心血管血流动力学的模拟来研究基本过程，但由于几种不确定性，缺乏对患者做出临床决策之前对个性化模拟结果的信任。这正是该提议的目的，调查了这些不确定性并开发了允许明智的医疗决定的技术。 Co-Pis建议将其计算工具作为开源程序传播。尽管众所周知，心血管模拟需要大量的假设和不确定的临床数据同化，但这些不确定性目前被扫除在地毯下，要求最终用户接受确定性的模拟预测为“真相”，而没有相关的统计数据。结果，研究人员和临床医生想知道“根据众多不确定性，模拟预测的可靠性如何？”和“有关输出预测的统计数据如何随着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中有理的怀疑，这是采用的障碍。开发变革性技术以评估目前缺乏该领域的不确定性，对于安全和常规采用个性化医学和生物力学研究的模拟至关重要。这是该提案涵盖的领域，因为它渴望开发可导致数据驱动的心血管模型纳入围绕药物治疗，设备放置，手术方法和干预措施的决策的技术。该提案有两个目标：1）开发快速自动化的方法，用于参数估计并将不确定数据吸收到多尺度模型中，2）开发一个有效的框架，以传播从临床和成像数据到模拟预测的不确定性。提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定量框架（UQ）框架，尽管该框架将适用于广泛的其他心血管和呼吸道疾病。模拟将使用多级并行算法结构在高性能计算（HPC）环境中运行。最终目标是解决心血管模拟中有关可靠性和鲁棒性的目前未解决的问题。如果成功的话，这项工作的结果将实现计算模型并建立可靠性指标，以指导模型改进和数据收集。心血管模拟有可能个性化单个患者的治疗方法，并表征体内机械环境，提供关键的生物力学数据，这些数据无法轻易从医学成像中获得。提出的计算框架可以适用于使用计算流体动力学的生物医学计算，生物建模和工程应用中的一系列问题。将通过对辛伯管开源项目的贡献来实现传播，马斯登博士是PI。提出了通过在研究生级课程中引入统计概念以及通过向中学生推广的统计概念来整合研究和教学的活动。