While qualitative wall motion analysis has proven valuable in clinical cardiology practice, quantitative analyses remain too time-consuming for routine clinical use. Our long-term goal is therefore to develop automated methods for quantitative wall motion analysis. In this paper, we utilize a finite element model of the regionally ischemic canine left ventricle to demonstrate a new approach based on parameterization of the left ventricular endocardial surface in prolate spheroidal coordinates. The parameterization provided a substantial data reduction and enabled simple definition, calculation, and display of three-dimensional fractional shortening (3DFS), a quantitative measure of wall motion analogous to the fractional shortening measure used in 2D analysis. The endocardial surface area displaying akinesis or dyskinesis by 3DFS corresponded closely to simulated ischemic region size and 3DFS identified appropriate wall motion abnormalities during experimental coronary occlusion in a canine pilot study. 3DFS therefore appears to be a reasonable candidate for clinical tests to determine its utility in identifying and quantifying acute regional ischemia in patients. By linking state of the art finite element models to the clinically relevant framework of wall motion analysis, the methods presented here will facilitate formulation, in silico prescreening, and clinical testing of additional candidate measures of wall motion.

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Stress echocardiography is an important screening test for coronary artery disease. Currently, cardiologists rely on visual analysis of left ventricular (LV) wall motion abnormalities, which is subjective and qualitative. We previously used finite-element models of the regionally ischemic left ventricle to develop a wall motion measure, 3DFS, for predicting ischemic region size and location from real-time 3D echocardiography (RT3DE). The purpose of this study was to validate these methods against regional blood flow measurements during regional ischemia and to compare the accuracy of our methods to the current state of the art, visual scoring by trained cardiologists. We acquired RT3DE images during 20 brief (<2 min) coronary occlusions in dogs and determined ischemic region size and location by microsphere-based measurement of regional perfusion. We identified regions of abnormal wall motion using 3DFS and by blinded visual scoring. 3DFS predicted ischemic region size well (correlation r 2 =0.64 against microspheres, p<0.0001), reducing error by more than half compared to visual scoring (8±9% vs. 19±14%, p<0.05), while localizing the ischemic region with equal accuracy. We conclude that 3DFS is an objective, quantitative measure of wall motion that localizes acutely ischemic regions as accurately as wall motion scoring while providing superior quantification of ischemic region size.

Abstract-Matrix-phased array transducers for real-time 3-D ultrasound enable fast, noninvasive visualization of cardiac ventricles. Typically, 3-D ultrasound images are semiautomatically segmented to extract the left ventricular endocardial surface at end-diastole and end-systole. Automatic segmentation and propagation of this surface throughout the entire cardiac cycle is a challenging and cumbersome task. If the position of the endocardial surface is provided at one or two time frames during the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of 3-D ultrasound data. In this paper, we applied a region-based tracking algorithm to track the endocardial surface between two reference frames that were manually segmented. To evaluate the tracking of the endocardium, the method was applied to 40 open-chest dog datasets with 484 frames in total. Ventricular geometry and volumes derived from region-based endocardial surfaces and manual tracing were quantitatively compared, showing strong correlation between the two approaches. Statistical analysis showed that the errors from tracking were within the range of interobserver variability of manual tracing. Moreover, our algorithm performed well on ischemia datasets, suggesting that the method is robust-to-abnormal wall motion. In conclusion, the proposed optical flow-based surface tracking method is very efficient and accurate, providing dynamic "interpolation" of segmented endocardial surfaces.

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. A finite element surface was fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency between manual tracing and optical flow estimation on 73% of the entire surface with fewer than 10% difference. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in threedimensional ultrasound data sets.