CDS&E: A Validated Hybrid Echo-CFD Framework for Patient-Specific Cardiac Assessment

CDS

• 批准号: 2152869
• 负责人: Iman Borazjani
• 金额: $ 54.24万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152869&HistoricalAwards=false
• 关键词: CDS& Validated; Hybrid; Echo; CFD

A computational model of the heart, built upon medical images, is invaluable for assessing cardiac function, managing therapy, optimizing biomedical devices for a specific patient, or better understanding the disease when data from a population are available. 2D echocardiography (echo) is the main imaging modality for a noninvasive evaluation of heart function due to its fast acquisition time, lower costs, portability, and wider availability compared to other imaging modalities. However, a computational model built upon 2D echo to benefit from its advantages, circumvent limitations of echo in anatomic depictions and yet, provide reliable and clinically useful applications, does not exist today. In this project, a computational heart model generated from 2D echo scans will be developed and validated to replicate cardiac flow and function and compute in vivo tissue and electrophysiological properties for specific patients. Considering that echo is the top imaging choice for evaluating heart disease, the top killer in the US, accounting for about 21% of deaths in 2020, a hybrid echo-CFD framework is anticipated to be most impactful compared to a framework coupled with other imaging modalities. Undergraduate students, in addition to graduate student, will be involved in the research (e.g., delineating the echo images) to broaden the impact.The long-term objective of this research is to develop a software package that can be utilized easily, based on echo images, to help basic science and medical researchers model the heart to diagnose heart disease, devise treatment strategies, optimize medical devices (e.g., valves, left-ventricular assist devices (LVAD), pacemakers, etc.) for specific patients, and contribute to better understanding the disease when data from a population become available. The goal of this project is to create a validated computational pipeline that takes standard echo scans as input, models cardiac flow and function, i.e., a hybrid echo-CFD framework, and computes in vivo mechanical and electrophysiological properties for a specific patient. To achieve this objective and capitalize on previous work, the walls of heart chambers and their valves will be identified in the 2D echo using deep learning. The 3D geometry will be reconstructed, and valves/atria geometries will be optimized by adopting an averaged-geometrical model. The resulting 3D geometry generating code will be coupled with an in-house CFD code based on a sharp-interface immersed boundary method to simulate large-deformation, fluid-structure interaction problems. The convergence of the Newton-Krylov solver of the CFD is accelerated by using an initial guess predicted from deep learning methods. The in vivo properties will be obtained by solving the inverse problem. Animal studies will be performed to obtain local reference flow, pressure measurements, and echo scans to validate the computational framework. The method will also be tested on retrospective clinical scans (human data).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建立在医学图像上的心脏计算模型对于评估心脏功能、管理治疗、优化特定患者的生物医学设备或在获得人群数据时更好地理解疾病是非常宝贵的。与其他成像方式相比，2D超声心动图（回声）具有快速采集时间、较低成本、便携性和更广泛的可用性，是无创评估心脏功能的主要成像方式。然而，建立在2D回波上的计算模型，以受益于其优点，规避回波在解剖学上的限制，并且提供可靠的和临床上有用的应用，目前还不存在。在该项目中，将开发和验证从2D回波扫描生成的计算心脏模型，以复制心脏流量和功能，并计算特定患者的体内组织和电生理特性。考虑到超声心动图是评估心脏病的首选成像方法，心脏病是美国的头号杀手，2020年约占死亡人数的21%，与结合其他成像方式的框架相比，混合超声心动图-CFD框架预计最具影响力。 本科生，除了研究生，将参与研究（例如， 这项研究的长期目标是开发一个软件包，该软件包可以基于回波图像容易地使用，以帮助基础科学和医学研究人员对心脏进行建模以诊断心脏病，设计治疗策略，优化医疗设备（例如，瓣膜、左心室辅助装置（LVAD）、起搏器等）对于特定的患者，并有助于更好地了解疾病时，从人口的数据变得可用。该项目的目标是创建一个经过验证的计算管道，该管道将标准回波扫描作为输入，对心脏流量和功能进行建模，即，一个混合回声CFD框架，并计算在体内的机械和电生理特性为特定的病人。为了实现这一目标并利用以前的工作，将使用深度学习在2D回波中识别心室壁及其瓣膜。将重建3D几何结构，并通过采用平均几何模型优化瓣膜/心房几何结构。由此产生的3D几何生成代码将与基于尖锐界面浸没边界法的内部CFD代码相结合，以模拟大变形，流体-结构相互作用问题。通过使用从深度学习方法预测的初始猜测来加速CFD的Newton-Krylov求解器的收敛。体内特性将通过求解逆问题来获得。将进行动物研究，以获得局部参考流量、压力测量值和回波扫描，从而验证计算框架。该方法还将在回顾性临床扫描（人类数据）中进行测试。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。