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

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 冠状动脉疾病，通过在静息时和运动后对心脏的左心室（LV）进行成像， 药理学诱导的应激来揭示局部缺血。然而，收购严重依赖于运营商， 二维（2D），并且解释通常基于定性评估。虽然各种各样的泉- 在研究文献中已经提出了一些2D方法，但没有一种方法被证明是上级的 静止/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，我们建议，前进的道路必须集中在一个新的计算im- 定量四维（三维加时间）负荷超声心动图的年龄分析范例。我们 该策略整合了来自射频（RF）和B型超声心动图图像的信息 使用矩阵阵列探针获得。综合分析系统将产生准确和可靠的措施 应变和应变率-在休息，应力和休息和应力之间的差异-这将确定我的- 多巴酚丁胺诱导的应激后的卵巢组织风险。这项工作将涉及小说的发展（1） 相敏的、基于相关性的RF超声斑点跟踪，以估计中壁位移，（2）ma- chine学习技术，用于从B模式数据中定位LV边界表面及其位移，（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 估计应变和应变率的密集时空参数，以及（4）静止的非刚性配准， 应力图像序列，以开发独特的，3D差异变形参数。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量进行验证 狭窄。从4D负荷超声心动图导出的变形和差异变形的能力， 在存在现有梗死的情况下检测新的心肌组织处于危险中， 急性/慢性犬梗死模型叠加缺血。该技术将被翻译成胡- 通过测量我们的变形和差异变形参数的再现性来进行评估 在一小群受试者中。三个主要合作者将在这项工作中合作。一个由马修奥唐纳领导的小组 来自华盛顿大学的J.K.将开发基于RF的斑点跟踪方法。一个图像分析小组 由耶鲁大学的PI James邓肯领导，将开发分割，形状跟踪，密集 位移积分和应变计算。耶鲁大学阿尔伯特·西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研究以及人体应力回波研究。飞利浦的一位顾问 Medical Systems将与整个团队合作，为超声图像采集技术搭建桥梁。

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.