Machine Learning and Deformable Model-based 4D Characterization of Cardiac Dyssynchrony from MRI

摘要/摘要 在存在诸如缺血性心脏病(IHD)等疾病的情况下，心脏不同步会恶化心脏 而且往往得不到有效的治疗。然而，虽然心血管等成像方法 磁共振(CMR)可以提供高质量的运动心脏图像，与传统的临床 心功能的定量分析在很大程度上限于对左心室(LV)的整体功能分析， 只有定性和主观性的区域功能表征。更好地量化的障碍 局部功能是心脏壁复杂的3D结构和运动，这通常是必要的 耗时的用户引导的图像处理以执行相关联的3D运动分析。 用于图像分析的机器学习(ML)方法的最新进展有望成为一种新的方法 加快心脏图像的处理，以及分析潜在的区域运动模式。 然而，当前用于图像分析的Deep ML(DML)方法在很大程度上起到了黑匣子的作用，没有 明确哪些特征对分析结果的影响最大，从而限制了它们的临床应用。在……里面 在这项研究项目的最初资助期间，我们一直在制定综合方法，以 CMR数据的分割、三维重建和分析及其在心脏评价中的应用 不同步。今天，用心脏再同步治疗(CRT)治疗心力衰竭的不同步性导致 只有~2/3的患者按常规标准(通常是通过心电图)得到改善。 我们的初步结果显示，MRI对不同步的评估与 心脏再同步治疗(CRT)的结果。在新提出的研究中，我们将进一步发展 这些方法的目的是使心脏分析方法自动化。这将包括引入 新的基于ML的方法，它将纳入导致以下情况的特定心脏运动因素的信息 不同步的不同疾病状态的分类。我们的假设是，通过使用这些新的基于ML的 对于心脏运动分析的方法，我们将发现和评估显著的定量相关性 不同的心脏不同步运动模式和CRT结果之间的关系。此外，晚格拉多德 增强扫描(LGE)为脑梗塞的可视化提供图像。将组织特征化纳入到 运动模式分析可以增加对梗死区如何影响区域的理解。 与不同步一致的运动。这些发现的发现将使我们能够在未来验证它们 临床研究。 该项目还将传播我们的新颖、结合DML和基于模型的方法来量化和 对影响局部室壁运动的疾病的心脏运动进行分类。然后，其他研究小组可以应用我们的 专门研究不同步以及其他影响左室运动的心脏疾病的工具。

Summary/Abstract In the presence of diseases such as ischemic heart disease (IHD), cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. However, while imaging methods such as cardiovascular magnetic resonance (CMR) can provide high quality images of the moving heart, conventional clinical quantitative analysis of cardiac function is largely limited to global function analysis of the left ventricle (LV), with only qualitative and subjective characterization of regional function. An obstacle to better quantification of regional function is the complex 3D structure and motion of the heart wall, which has typically necessitated time-consuming user-guided processing of the images to carry out the associated 3D-motion analysis. Recent advances in machine-learning (ML) approaches for image analysis are promising as new means to speed up the processing of cardiac images, as well as to analyze the underlying regional motion patterns. However, current Deep ML (DML) approaches to image analysis largely function as “black boxes”, without clear indications of which features contribute most to the analysis results, thus limiting their clinical utility. In the initial funded period of this research project, we have been developing integrated approaches to the segmentation, 3D reconstruction, and analysis of CMR data, with application to the evaluation of cardiac dyssynchrony. Today, treatment of dyssynchrony in HF with cardiac resynchronization therapy (CRT) leads to improvement in only ~2/3 patients selected with conventional criteria (usually by electrocardiogram [ECG]). Our initial results show encouraging results of correlation between MRI evaluation of dyssynchrony and cardiac resynchronization therapy (CRT) outcomes. In the new proposed research, we will further develop these methods, with the goal of automating the cardiac analysis methods. This will include the introduction of new ML-based methods, which will incorporate information on the specific cardiac motion factors that lead to classification of different disease states in dyssynchrony. Our Hypothesis is that by using these new ML-based methods for cardiac motion analysis, we will discover and evaluate significant quantitative correlations between different cardiac dyssynchrony motion patterns and CRT outcomes. Also, late-gadolinium enhancement (LGE) provides images for infarction visualization. Incorporation of tissue characterization into the motion-pattern analysis could lead to increased understanding of how infarcted areas affect regional motion in concert with dyssynchrony. The unearthing of these findings will allow us to validate them in future clinical studies. The project will also disseminate our novel, coupled DML and model-based methodology for quantifying and classifying cardiac motion in diseases affecting regional wall motion. Other research groups can then apply our tools to specifically study dyssynchrony, as well as other cardiac diseases affecting LV motion.