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

基本信息

批准号：
9030537
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
项目类别：
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/9030537
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 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 Operative Surgical Procedures Output Patient Care Patient risk Patients Physiology Process Property Publications Reporting Reproducibility Research Research Personnel Risk Running Sampling Source Stratification Technology Testing Time Translations Uncertainty Work X-Ray Computed Tomography advanced simulation attenuation biocomputing clinical application clinical care clinical decision-making clinically relevant computer framework fundamental research hemodynamics imaging Segmentation imaging modality improved innovation interest interpatient variability mathematical methods model development mortality multi-scale modeling novel novel strategies open source personalized medicine public health relevance simulation statistics tool

项目摘要

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已纳入临床决策、手术计划和 FDA 批准流程中。因此，这些模拟有可能影响改变患者生活的决策。进步伴随着患者和治疗他们的战士越来越多的责任，以证明模拟产生可靠和安全的结果。对于模拟界来说，继续推动常规临床使用是危险和不负责任的。患者特定的多尺度模型，无需提供意味着量化他们预测的可靠性。目前缺乏评估不确定性的技术，这将降低患者风险，并最终实现个性化医疗模拟的安全和常规使用。患者特定的心血管（CV）模拟需要结合不确定的假设以及来自临床和影像数据的输入。目前被掩盖了，要求最终用户接受确定性模拟预测作为“真理”，没有相关的置信区间，这导致临床界对模拟的可信度产生合理的怀疑，并且成为临床使用和最终的障碍。 FDA我们建议通过创建一套高效、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，从而解决这一未满足的需求。我们将通过应用于冠状动脉的多尺度模拟来建立我们的 UQ 框架。冠状动脉建模是昆士兰大学方法学的理想测试平台和挑战，具有图像分割、材料特性和复杂生理学产生的多参数不确定性。为了实现我们的目标，我们提出了三个具体目标：1）综合多模态成像研究，将提高模型保真度并实现不确定性评估，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) 开发有效的计算框架来量化 CAD 模拟中的不确定性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) 提供一套新颖的套件它是创新性的，因为 (1) UQ 是为了建立模拟输出的置信区间，以及 (2) CV 模拟社区中通常未讨论的无数不确定性被严格量化。学科团队由在患者特异性建模、昆士兰大学数学方法、高性能计算和医学成像方面拥有丰富专业知识的研究人员组成。我们在联合发表、临床翻译和资助合作方面拥有良好的记录。我们的转化目标是提供心血管模拟社区昆士兰大学的高效工具，提高了模拟可靠性的标准，并最终提高了临床采用率。