Publications by authors named "Christopher M Ingrassia"

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.

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Myocardial elastography (ME), a radio frequency (RF)-based speckle tracking technique with one-dimensional (1-D) cross correlation and novel recorrelation methods in a 2-D search was proposed to estimate and fully image 2-D transmural deformation field and to detect abnormal cardiac function. A theoretical framework was first developed in order to evaluate the performance of 2-D myocardial elastography based on a previously developed 3-D finite-element model of the canine left ventricle. A normal (control) and an ischemic (left-circumflex, LCx) model, which more completely represented myocardial deformation than a kinematic model, were considered.

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Quantitative measurement of left ventricular wall motion can improve clinical diagnosis by providing a more objective approach than qualitative analysis, which is subject to large inter-observer variability. We have developed novel techniques for quantifying left ventricular wall motion in three-dimensional image data sets. In this study, finite element models simulating regional ischemia in the left ventricle were used to screen potential wall motion measures for their capability to detect and evaluate the size of an ischemic region.

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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.

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