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: 10417165
• 负责人: Subhi AlAref
• 金额: $ 73.19万
• 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/10417165
• 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 magnetic resonance imaging; 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; machine learning model; novel; patient subsets; reconstruction; response; spatiotemporal; success; therapy outcome; three dimensional structure; tool; 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 magnetic resonance imaging; 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; machine learning model; 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.

摘要/摘要 在存在缺血性心脏病（IHD）等疾病的情况下，心脏异常障碍检测心脏 功能，通常无法有效治疗。但是，诸如心血管等成像方法 磁共振（CMR）可以提供移动心脏，常规临床的高质量图像 心脏功能的定量分析在很大程度上仅限于左心室（LV）的全局功能分析， 仅具有定性和主观的区域功能表征。更好地量化的障碍 区域功能是心脏壁的复杂的3D结构和运动，通常是必要的 图像的耗时用户引导的处理以执行相关的3D运动分析。 机器学习的最新进展（ML）用于图像分析的方法是承诺作为新手段 加快心脏图像的处理，并分析潜在的区域运动模式。 但是，当前的深度ML（DML）方法用于图像分析主要起作用的“黑匣子”，而无需 清楚的迹象表明哪些特征对分析结果有最大的作用，从而限制了其临床实用性。在 该研究项目的最初资助时期，我们一直在开发综合方法 CMR数据的细分，3D重建和分析，并应用心脏评估 异议。如今，通过心脏重新同步治疗（CRT）在HF中的异位障碍治疗导致 仅选择具有常规标准的〜2/3患者（通常是通过心电图[ECG]）的改善。 我们的初步结果表明，MRI评估与同步和 心脏重新同步疗法（CRT）结局。在新提出的研究中，我们将进一步发展 这些方法，目的是自动化心脏分析方法。这将包括引入 新的基于ML的方法将结合有关导致特定心脏运动因子的信息 在异质症中的不同疾病状态的分类。我们的假设是使用这些新的基于ML 心脏运动分析的方法，我们将发现并评估显着的定量相关性 在不同的心脏异位运动模式和CRT结果之间。另外，晚期加德利顿 增强（LGE）提供了用于梗塞可视化的图像。将组织表征纳入 动作图案分析可能会提高人们对梗塞区域如何影响区域的了解 与Dyssynchrony共同运动。这些发现的发掘将使我们将来对它们进行验证 临床研究。 该项目还将传播我们的小说，耦合的DML和基于模型的方法来量化和 在影响区域壁运动的疾病中对心脏运动进行分类。然后，其他研究小组可以应用我们的 专门研究失调的工具以及其他影响LV运动的心脏疾病。