Integrated RF and B-mode Deformation Analysis for 4D Stress Echocardiography

用于 4D 应力超声心动图的集成 RF 和 B 模式变形分析

• 批准号: 8614454
• 负责人: JAMES S DUNCAN
• 金额: $ 81.97万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-18 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8614454
• 关键词: Acute; Autopsy; Bayesian Modeling; Canis familiaris; Cardiology; Chest; Chronic; Clinical; Code; Collection; Coronary Arteriosclerosis; Data; Detection; Development; Diagnosis; Diastole; Dictionary; Dimensions; Disease; Dobutamine; Dose; Echocardiography; Exercise; Four-Dimensional Echocardiography; Frequencies; Heart; Human; Hybrids; Image; Image Analysis; Imaging technology; Implant; Infarction; Ischemia; Lead; Learning; Left; Left ventricular structure; Literature; Machine Learning; Manuals; Measures; Medical; Methodology; Methods; Microspheres; Modality; Modeling; Motion; Myocardial; Myocardial Ischemia; Myocardial tissue; Patients; Perfusion; Phase; Physiology; Plague; Process; Radial; Radio; Reader; Reporting; Reproducibility; Research; Resolution; Rest; Risk; Shapes; Signal Transduction; Stenosis; Stress; Stress Echocardiography; Surface; System; Systems Analysis; Techniques; Technology; Testing; Time; Tissues; Translating; Ultrasonography; United States; Universities; Ventricular; Visual; Washington; Work; base; clinical decision-making; cohort; cost effective; cost efficient; data integration; elastography; in vivo; novel; novel strategies; public health relevance; radiofrequency; single photon emission computed tomography; spatiotemporal; two-dimensional

Project Summary/Abstract Stress echocardiography is a clinically established, cost-effective technique for detecting and characterizing coronary artery disease by imaging the left ventricle (LV) of the heart at rest and then after either exercise or pharmacologically-induced stress to reveal ischemia. However, acquisitions are heavily operator dependent, two-dimensional (2D), and interpretation is generally based on qualitative assessment. While a variety of quan- titative 2D approaches have been proposed in the research literature, none have been shown to be superior to the still highly variable qualitative visual comparison of rest/stress echocardiographic image sequences for detecting ischemic disease. Here, we propose that the way forward must focus on a new computational im- age analysis paradigm for quantitative 4D (three spatial dimensions plus time) stress echocardiography. Our strategy integrates information derived from both radiofrequency (RF) and B-mode echocardiographic images acquired using a matrix array probe. The integrated analysis system will yield accurate and robust measures of strain and strain rate - at rest, stress and differentiallly between rest and stress - that will identify my- ocardial tissue at-risk after dobutamine-induced stress. This work will involve the development of novel (1) phase-sensitive, correlation-based RF ultrasound speckle tracking to estimate mid-wall displacements, (2) ma- chine learning techniques to localize the LV bounding surfaces and their displacements from B-mode data, (3) a meshless integration approach based on radial basis functions (RBFs) and Bayesian reasoning/sparse coding to estimate dense spatiotemporal parameters of strain and strain rate and (4) non-rigid registration of rest and stress image sequences to develop unique, 3D differential deformation parameters. The quantitative approach will be validated with implanted sonomicrometers and microsphere-derived flows using an acute canine model of stenosis. The ability of deformation and differential deformation derived from 4D stress echocardiography to detect new myocardial tissue at-risk in the presence of existing infarction will then be determined in a hybrid acute/chronic canine model of infarction with superimposed ischemia. The technique will be translated to hu- mans and evaluated by measuring the reproducibility of our deformation and differential deformation parameters in a small cohort of subjects. Three main collaborators will team on this work. A group led by Matthew O'Donnell from the University of Washington will develop the RF-based speckle tracking methods. An image analysis group led by the PI James Duncan at Yale University will develop methods for segmentation, shape tracking, dense displacement integration and strain computation. A cardiology/physiology group under Dr. Albert Sinusas at Yale will perform the acute and chronic canine studies and the human stress echo studies. A consultant from Philips Medical Systems will work with the entire team to bridge the ultrasound image acquisition technology.

项目概要/摘要 负荷超声心动图是一种临床上建立的、具有成本效益的技术，用于检测和表征 通过对休息时心脏的左心室 (LV) 进行成像，然后在运动或运动后进行冠状动脉疾病的成像 药理学诱导的应激以揭示缺血。然而，收购很大程度上依赖于运营商， 二维 (2D)，解释通常基于定性评估。虽然各种全 研究文献中已经提出了初步的二维方法，但没有一个被证明是优越的 与静息/应激超声心动图图像序列的仍然高度可变的定性视觉比较 检测缺血性疾病。在这里，我们建议前进的道路必须集中在新的计算模型上 定量 4D（三个空间维度加时间）负荷超声心动图的年龄分析范例。我们的 策略整合了来自射频 (RF) 和 B 型超声心动图图像的信息 使用矩阵阵列探头获得。集成分析系统将产生准确而稳健的测量结果 应变和应变率 - 在休息、压力以及休息和压力之间的差异 - 这将识别我的- 多巴酚丁胺诱导的应激后心组织处于危险之中。这项工作将涉及小说的开发 (1) 相敏、基于相关的射频超声散斑跟踪来估计中壁位移，（2）ma- 用于定位 LV 边界表面及其来自 B 模式数据的位移的学习技术，(3) 基于径向基函数 (RBF) 和贝叶斯推理/稀疏编码的无网格积分方法 估计应变和应变率的密集时空参数，以及（4）静止和应变的非刚性配准 应力图像序列以开发独特的 3D 微分变形参数。定量方法 将使用急性犬模型通过植入式声测微计和微球衍生流进行验证 的狭窄。 4D 应力超声心动图产生的变形和差异变形的能力 在存在现有梗塞的情况下检测新的有风险的心肌组织，然后将在混合中确定 伴有叠加缺血的急性/慢性犬梗塞模型。该技术将被转化为hu- 并通过测量变形和差异变形参数的再现性进行评估 在一小群受试者中。三位主要合作者将合作开展这项工作。马修·奥唐纳领导的小组 华盛顿大学的研究人员将开发基于射频的散斑跟踪方法。图像分析组 由 PI James Duncan 领导的耶鲁大学将开发分割、形状跟踪、密集的方法 位移积分和应变计算。耶鲁大学 Albert Sinusas 博士领导下的心脏病学/生理学小组 将进行急性和慢性犬类研究以及人类压力回波研究。飞利浦顾问 医疗系统公司将与整个团队合作，搭建超声图像采集技术的桥梁。