Development of Image Analysis Algorithms for Computational Assessment of Cardiac Structure and Function

用于心脏结构和功能计算评估的图像分析算法的开发

• 批准号: RGPIN-2016-06270
• 负责人: Ukwatta, Eranga
• 金额: $ 2.62万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765874
• 关键词: Development; Image; Analysis; Algorithms; Computational

Advancements in cardiac magnetic resonance (MR) imaging technologies in the past decade have enabled great potential for developing novel measurements to detect abnormalities in the structure and function of the heart. Two such advancements include acquisition of high-resolution (Hi-res) three-dimensional (3D) images with increased spatial resolution, and quantitative imaging techniques to differentiate injured (infarcted) tissue from healthy tissue in the cardiac muscle. Tissue in infarcted regions plays a central role in the mechanisms that lead to heart dysfunction and therefore, accurate and reproducible estimation of the infarct regions is paramount to the analysis of cardiac structure and function. Parallel to these developments, computational modeling of cardiac function has emerged as a promising tool to predict abnormal electrical activity of the heart. These computational models are built based on the information extracted from cardiac MR images and must incorporate accurate geometries of the ventricles and infarcted regions of the heart for accurate prediction. This presents us with an opportunity to evaluate whether incorporating Hi-res, quantitative imaging methods in building computational models, as compared to the models generated from conventional low-resolution images, will improve the sensitivity and specificity in predicting abnormal electrical activities of the heart. However, in order to extract geometric information from these 3D MR images, the development of robust, automated image analysis methods is required. The overall goal of the research program will be to address this need by developing advanced image analysis tools for the non-invasive assessment of the cardiac structure and its function. In particular, image analysis methods will be developed to align cardiac MR images to a common reference to correct for cardiac motion and extract the 3D ventricular and infarct geometry of the heart. Computational simulations of the electrical activity of the heart will then be used to evaluate efficacy of the 3D MR technique. This research program will integrate emerging advances in cardiac MR imaging and computational cardiology through novel developments in medical image analysis. The anticipated significance of this work to biomedical engineering and imaging science is twofold: engineering innovations resulting from the development of new image analysis methods for 3D MR images; and enabling our understanding of accurate methods to image the infarct structure. By accurately incorporating the infarct structure, these virtual electrophysiological models may provide a tool to non-invasively interrogate the heart without the need for invasive catheter-based methods. Although the proposed algorithms will initially be developed for cardiac MR images, they will be directly applicable to analyzing images of other imaging techniques and organs.

在过去的十年中，心脏磁共振(MR)成像技术的进步使得开发新的测量方法来检测心脏结构和功能的异常具有巨大的潜力。两个这样的进步包括获得空间分辨率更高的高分辨率(Hi-res)三维(3D)图像，以及区分心肌受损(梗死)组织和健康组织的定量成像技术。梗死区的组织在导致心脏功能障碍的机制中起着核心作用，因此，准确和可重复地估计梗死区对于分析心脏的结构和功能至关重要。与这些发展相平行的是，心脏功能的计算模型已经成为预测心脏异常电活动的一种有前途的工具。这些计算模型是基于从心脏磁共振图像中提取的信息而建立的，并且必须包含准确的脑室和心脏梗塞区的几何形状以进行准确的预测。这为我们提供了一个机会来评估与传统的低分辨率图像生成的模型相比，在建立计算模型时结合高分辨率、定量成像方法是否将提高预测心脏异常电活动的敏感性和特异性。然而，为了从这些3D MR图像中提取几何信息，需要开发稳健的、自动化的图像分析方法。该研究计划的总体目标将是通过开发先进的图像分析工具来满足这一需求，以非侵入性地评估心脏结构及其功能。特别是，将开发图像分析方法，将心脏磁共振图像与公共参考对准，以校正心脏运动并提取心脏的3D脑室和梗塞几何图形。然后将使用心脏电活动的计算机模拟来评估3D MR技术的有效性。这项研究计划将通过医学图像分析的新发展，整合心脏磁共振成像和计算心脏病学的新进展。这项工作对生物医学工程和成像科学的预期意义是双重的：开发新的3D MR图像图像分析方法带来的工程创新；以及使我们能够理解准确的方法来成像梗塞结构。通过准确地结合梗死结构，这些虚拟电生理模型可以提供一种非侵入性询问心脏的工具，而不需要基于侵入性导管的方法。虽然所提出的算法最初将用于心脏磁共振图像，但它们将直接适用于分析其他成像技术和器官的图像。