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

基于机器学习和可变形模型的 MRI 心脏不同步 4D 表征

• 批准号: 10052934
• 负责人: Subhi AlAref
• 金额: $ 76.35万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10052934
• 关键词: 3-Dimensional; Affect; Area; Attention; Automation; Cardiac; Cardiovascular Physiology; Cardiovascular system; Classification; Clinical; Clinical Research; Complex; Consumption; Coupled; Data; Data Analyses; Data Set; Disease; EKG QRS Complex; Echocardiography; Electrocardiogram; Evaluation; Functional disorder; Funding; Future; Gadolinium; Goals; Guidelines; Heart; Heart Diseases; Heart failure; Image; Image Analysis; Image Enhancement; Infarction; Ischemia; Lead; Left ventricular structure; Location; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Methodology; Methods; Modeling; Motion; Myocardial Ischemia; Outcome; Output; Patient Selection; Patients; Pattern; Performance; Physiology; Prospective Studies; Pump; Research; Research Project Grants; Resolution; Sampling; Schedule; Selection Criteria; Speed; Sweden; Testing; Therapeutic; Time; Tissues; Treatment outcome; Universities; Visualization; Width; base; cardiac resynchronization therapy; deep learning; effective therapy; heart function; heart imaging; heart motion; image processing; imaging approach; imaging modality; improved; improved outcome; learning strategy; machine learning method; novel; patient subsets; reconstruction; response; spatiotemporal; success; therapy outcome; three dimensional structure; tool

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.

总结/摘要 在存在诸如缺血性心脏病（IHD）的疾病的情况下，心脏不同步恶化心脏功能。 功能正常，往往不能有效治疗。然而，虽然成像方法，如心血管 磁共振（CMR）可以提供运动心脏的高质量图像，常规临床 心脏功能的定量分析主要限于左心室（LV）的整体功能分析， 对区域功能的定性和主观描述。更好地量化 局部功能是心脏壁的复杂3D结构和运动，这通常需要 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作为新的手段， 加速心脏图像的处理，以及分析潜在的区域运动模式。 然而，当前用于图像分析的深度ML（DML）方法在很大程度上充当“黑匣子”， 明确指出哪些特征对分析结果贡献最大，从而限制了其临床实用性。在 在这个研究项目的最初资助期间，我们一直在开发综合方法， CMR数据的分割、3D重建和分析，并应用于心脏 不同步今天，心脏起搏治疗（CRT）治疗HF的不同步性导致 只有约2/3的患者根据常规标准（通常通过心电图[ECG]）选择。 我们的初步结果显示了令人鼓舞的结果之间的相关性MRI评估不同步， 心脏起搏治疗（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。这将包括引进 新的基于ML的方法，它将包含导致心脏运动的特定心脏运动因素的信息， 不同疾病状态的分类不同步。我们的假设是，通过使用这些新的基于ML的 心脏运动分析的方法，我们将发现和评估显着的定量相关性 不同的心脏不同步运动模式和CRT结果之间的关系。此外，晚钆 增强（LGE）提供用于梗塞可视化的图像。将组织表征纳入 运动模式分析可以增加对梗塞区域如何影响区域性的理解， 运动与不同步。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新颖的，耦合的DML和基于模型的方法，用于量化和 对影响局部室壁运动的疾病中的心脏运动进行分类。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LV运动的心脏疾病的工具。