Enabling reliable cardiovascular simulations via uncertainty quantification

通过不确定性量化实现可靠的心血管模拟

• 批准号: 9348646
• 负责人: Alison L Marsden
• 金额: $ 38.84万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-07 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9348646
• 关键词: Address; Adoption; Adult; Algorithms; Anatomy; Assimilations; Beds; Cardiac; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Childhood; Clinical; Clinical Data; Collaborations; Communities; Complex; Confidence Intervals; Coronary; Coronary Arteriosclerosis; Dangerousness; Data; Development; Dimensions; Disease; Documentation; Echocardiography; Ensure; Evaluation; Functional Imaging; Funding; Geometry; Goals; Hand; Heart; High Performance Computing; Image; Joints; Life; Magnetic Resonance Imaging; Markov Chains; Measurement; Medical Imaging; Methodology; Methods; Modality; Modeling; Morbidity - disease rate; Multimodal Imaging; Operative Surgical Procedures; Output; Patient Care; Patient risk; Patients; Physiology; Process; Property; Publications; Reporting; Research; Research Personnel; Risk; Risk stratification; Running; Sampling; Source; Technology; Testing; Time; Uncertainty; Work; X-Ray Computed Tomography; advanced simulation; attenuation; biocomputing; clinical application; clinical care; clinical decision-making; clinical imaging; clinical translation; clinically relevant; computer framework; fundamental research; hemodynamics; imaging Segmentation; imaging modality; imaging study; improved; innovation; interest; interpatient variability; mathematical methods; model development; mortality; multi-scale modeling; multidisciplinary; novel; novel strategies; open source; personalized medicine; public health relevance; scientific computing; simulation; statistics; tool

 DESCRIPTION (provided by applicant): Advanced simulations of cardiovascular hemodynamics and physiology are now being incorporated into clinical decision-making, surgical planning, and the FDA approval process. Simulations have potential to influence life- altering decisions for patients. As a result, these advancements come with an ever-increasing responsibility to the patients and the clinicians who treat them to prove that simulations produce reliable and safe results. It is dangerous and irresponsible for the simulation community to continue to push for routine clinical use of patient-specific multiscale models without providing a means to statistically quantify the reliability of their predictions. Development of transformative technology to assess uncertainty, which is currently lacking, will mitigate patient risk and ultimately enable safe and routine use of simulations for personalized medicine. Patient specific cardiovascular (CV) simulations require a combination of uncertain assumptions and inputs from clinical and imaging data. This issue currently gets swept under the rug, asking end-users to accept deterministic simulation predictions as "truth" with no associated confidence intervals. This leads to justified skepticism in the clinical community regarding the trustworthiness of simulations, and is a roadblock to clinical use and eventual FDA approval. We propose to address this unmet need by creating a suite of efficient and automated uncertainty quantification (UQ) tools to assess and improve the reliability of patient-specific simulation predictions. We wil establish our UQ framework through application to multiscale simulations of coronary artery disease (CAD). Coronary modeling is an ideal test-bed and challenge for UQ methodologies, with multi-parameter uncertainty arising from image segmentation, material properties, and complex physiology. To accomplish our objectives, we propose three specific aims: 1) An integrative multi- modality imaging study that will increase model fidelity and enable uncertainty assessment, 2) Creation of automated parameter-estimation tools for assimilation of clinical data into cardiovascular simulations, and 3) Development of an efficient computational framework to quantify uncertainties in simulations of CAD. The proposed work is significant because we will (1) raise the bar for the CV simulation community to report output statistics, (2) establish standards for adoption of simulations in clinical care and by other researchers, and (3) provide a novel suite of tools through the open-source SimVascular project. It is innovative because (1) UQ is performed to establish confidence intervals on simulation outputs and (2) the myriad uncertainties typically un- discussed in the CV simulation community are rigorously quantified. Our multi-disciplinary team consists of investigators with expertise in patient-specifi modeling, mathematical methods for UQ, high-performance computing, and medical imaging. We have a strong track record of joint publication, clinical translation, and funded collaborations Our translational goal is to provide the cardiovascular simulation community with efficient tools for UQ, raising the bar for simulation reliability and ultimately increasing clinical adoption.

 描述(由申请人提供)：心血管血流动力学和生理学的高级模拟现在正在被纳入临床决策、手术计划和FDA的批准过程。模拟有可能影响患者改变生活的决定。因此，这些进步伴随着患者和治疗他们的临床医生越来越多的责任，以证明模拟产生可靠和安全的结果。模拟社区继续推动患者特定多尺度模型的常规临床使用，而不提供 对他们预测的可靠性进行统计量化的方法。变革性发展 目前缺乏的评估不确定性的技术将降低患者的风险，并最终使个性化医疗模拟的安全和常规使用成为可能。患者特定的心血管(CV)模拟需要结合不确定的假设和来自临床和成像数据的输入。这个问题目前被掩盖了，要求最终用户接受确定性的模拟预测，认为这是没有关联的可信区间的“真理”。这导致了临床社区对模拟的可信性的合理怀疑，并成为临床使用和最终FDA批准的障碍。我们建议通过创建一套高效和自动化的不确定性量化(UQ)工具来评估和提高患者特定模拟预测的可靠性，以解决这一未得到满足的需求。我们将通过应用于冠状动脉疾病(CAD)的多尺度模拟来建立我们的UQ框架。冠状动脉建模是UQ方法的理想试验台和挑战，图像分割、材料特性和复杂的生理过程带来了多参数的不确定性。为了实现我们的目标，我们提出了三个具体的目标：1)一个综合性的多模式成像研究，将提高模型的保真度并使不确定性评估成为可能；2)创建自动参数估计工具，用于将临床数据同化到心血管模拟中；以及3)开发一个有效的计算框架来量化CAD模拟中的不确定性。这项拟议的工作意义重大，因为我们将(1)提高CV模拟社区报告输出统计数据的标准，(2)为临床护理和其他研究人员采用模拟建立标准，以及(3)通过开源的SimVato项目提供一套新的工具。它是创新的，因为(1)UQ被用来建立仿真输出的置信度区间，(2)在CV仿真社区中通常不讨论的无数不确定性被严格地量化。我们的多学科团队由在患者特定建模、UQ数学方法、高性能计算和医学成像方面拥有专业知识的研究人员组成。我们在联合出版、临床翻译和资助合作方面有着良好的记录。我们的翻译目标是为心血管模拟社区提供高效的UQ工具，提高模拟可靠性的标准，并最终增加临床采用率。