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

项目摘要/摘要 负荷超声心动图是一种临床确立的、经济有效的检测和表征技术。 通过对静息状态下的心脏左室(LV)进行成像，然后在运动或运动后 药物诱导的应激反应显示为缺血。然而，收购严重依赖于运营商， 二维(2D)，解释一般以定性评估为基础。而各种拳击-- 在研究文献中已经提出了有标题的2D方法，没有一种方法被证明是优越的 对静息/负荷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，我们提出前进的方向必须集中在新的计算信息-- 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式。我们的 策略集成了来自射频(RF)和B型超声心动图图像的信息 使用矩阵阵列探头采集。综合分析系统将产生准确和可靠的措施 应变和应变率-在静止、应力以及静止和应力之间的差别-这将识别我的- 多巴酚丁胺应激后心肌组织处于危险状态。这项工作将涉及小说的发展(1) 相敏的，基于相关的射频超声散斑跟踪，以估计中壁位移，(2)MA- 从B-型数据定位LV边界表面及其位移的中国学习技术，(3) 基于径向基函数和贝叶斯推理/稀疏编码的无网格积分方法 估计密集的应变和应变率时空参数以及(4)REST和REST的非刚性配准 应力图像序列，以开发独特的3D差分变形参数。数量化方法 将使用植入的声波测微仪和微球衍生的流动来验证急性犬科动物模型 狭窄的症状。4D负荷超声心动图的变形能力和差异变形能力 在存在现有心肌梗死的情况下检测新的危险心肌组织将在混合 急性/慢性犬急性/慢性脑梗塞叠加缺血模型。这项技术将被翻译成HU- 通过测量我们的变形和差分变形参数的重现性来进行评估 在一小群受试者中。三个主要的合作者将在这项工作上合作。由马修·奥唐奈领导的一个小组 将开发基于射频的散斑跟踪方法。一个图像分析小组 由耶鲁大学的Pi James Duncan领导的将开发出分割、形状跟踪、密集 位移积分和应变计算。耶鲁大学阿尔伯特·辛努萨斯博士领导的心脏病学/生理学小组 将进行急性和慢性犬类研究和人类应激回声研究。飞利浦的一位顾问 医疗系统公司将与整个团队合作，架起超声图像采集技术的桥梁。

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.