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

Yale University, New Haven CT

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Abstract

DESCRIPTION (provided by applicant): 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-induce 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 quantitative 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 image 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 differentially between rest and stress - that will identify myocardial tissue at-risk after dobutamine-induced stress. This work wil 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 humans 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.