Collaborative Research: SCH: Quantifying Cardiac Performance by Measuring Myofiber Strain with Routine MRI

合作研究：SCH：通过常规 MRI 测量肌纤维应变来量化心脏性能

基本信息

批准号：
2205043
负责人：
Luigi Perotti
金额：
$ 69.63万
依托单位：
The University of Central Florida Board of Trustees
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205043&HistoricalAwards=false
关键词：
Collaborative Research SCH Quantifying Cardiac

项目摘要

The goal of this research is to develop a new method to quantify cardiac performance in patients affected by cardiac diseases. Current strategies to evaluate cardiac performance often rely on inadequate global measures, such as ejection fraction, which are non-specific and often late outcomes. Cardiac motion is driven by billions of heart cells acting together, whose contraction and relaxation can be measured using myofiber strain. Myofiber strain is therefore a direct measure of cardiac function and is an ideal candidate to evaluate cardiac performance, improving diagnosis and therapy planning. However, there are three main obstacles that hinder the deployment of myofiber strain in a clinical setting: (i) There is no method to reliably compute myofiber strain from images that are routinely acquired; (ii) There are no reliable error estimates for the evaluated strains, preventing their use to distinguish between health and disease; and (iii) There is no framework to compute myofiber strain on demand without hardware and technical barriers. This project aims at overcoming these obstacles by combining computational modeling and artificial intelligence with readily available magnetic resonance imaging. The transition to the clinic will be highly facilitated by deploying the proposed framework in a completely online platform leveraging on-demand cloud computing. Investigators around the globe will be able to test remotely the newly proposed technology without the need for specific hardware or additional software. The multidisciplinary research carried out in this project will train the next generation of scientists, who will be capable of carrying out projects in smart health and biomedical research at the forefront of medical imaging, artificial intelligence, and computational modeling. The proposed approach will estimate myofiber strain by minimizing the difference between computed and measured surface cardiac motion. Measured surface motion is extracted from cine Magnetic Resonance Imaging (MRI), which is routinely acquired in a clinical MRI setting. Computed left ventricular surface motion is obtained by solving a computational kinematics model based on the biomechanics of myofiber shortening and relaxation. Uncertainty in myofiber strain predictions will be evaluated based on imaging data noise and model assumptions. Fast and accurate high-fidelity models and Bayesian error estimators will propagate experimental and model uncertainties to establish confidence in myofiber strain estimates. As a results, the generated models will allow to characterize strains’ uncertainty and variation in healthy and diseased individuals. The proposed approach will be demonstrated and validated in a pilot study to aid therapy planning in patients affected by aortic stenosis. This new approach paves the way to improve diagnosis, prognosis, and therapy planning for patients affected by a wide range of cardiomyopathies resulting in compromised left ventricular function and therefore myofiber mechanics.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.

这项研究的目的是开发一种新方法来量化受心脏病影响的患者的心脏表现。当前评估心脏性能的策略通常取决于全球措施不足，例如排放量，这些措施是非特异性且通常是较晚的结果。心脏运动是由数十亿心脏细胞一起作用在一起的，它们的收缩和放松可以使用肌纤维菌株测量。因此，肌纤维菌株是对心脏功能的直接测量，是评估心脏表现，改善诊断和治疗计划的理想候选者。但是，有三个主要的障碍阻碍了临床环境中肌纤维菌株的部署：（i）没有方法可以从通常获得的图像中可靠的计算肌纤维菌株；（ii）评估菌株没有可靠的误差估计，从而阻止了它们用于区分健康和疾病；（iii）没有框架可以在没有硬件和技术障碍的情况下按需计算肌纤维应变。该项目旨在通过将计算建模和人工智能与随时可用的磁共振成像结合起来来克服这些障碍。通过在利用按需云计算的完全在线平台中部署所提出的框架，将对诊所的过渡得到高度支持。全球的调查人员将能够远程测试新提出的技术，而无需特定的硬件或其他软件。该项目中进行的多学科研究将培训下一代科学家，他们将能够在医学成像，人工智能和计算建模的最前沿进行智能健康和生物医学研究项目。所提出的方法将通过最大程度地减少计算出的表面心脏运动和测量的表面运动之间的差异来估计肌纤维应变。测得的表面运动是从Cine磁共振成像（MRI）中提取的，计算出的左心室表面运动是通过基于肌纤维缩短和放松的生物力学来求解计算运动学模型来获得的。肌纤维应变预测的不确定性将根据成像数据噪声和模型假设评估。快速准确的高保真模型和贝叶斯误差估计器将传播实验和模型不确定性，以建立对肌纤维应变估计的信心。结果，生成的模型将允许表征菌株的不确定性和健康和厌恶个体的变化。该方法将在一项试点研究中证明和验证，以帮助受主动脉狭窄影响的患者的治疗计划。这种新方法为受到广泛心肌病影响的患者改善诊断，预后和治疗计划铺平了道路，导致左心室功能受损，因此肌化机制受损。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并通过评估诚实地进行了评估。