University of Pennsylvania

Paul A Yushkevich

Robert C Gorman

Joseph H Gorman, Iii

Chandra M Sehgal

Benjamin M Jackson

Alison M Pouch

Brian B Avants

Hongzhi Wang

A method is provided for measuring or estimating stress distributions on heart valve leaflets by obtaining three-dimensional images of the heart valve leaflets, segmenting the heart valve leaflets in the three-dimensional images by capturing locally varying thicknesses of the heart valve leaflets in three-dimensional image data to generate an image-derived patient-specific model of the heart valve leaflets, and applying the image-derived patient-specific model of the heart valve leaflets to a finite element analysis (FEA) algorithm to estimate stresses on the heart valve leaflets. The images of the heart valve leaflets may be obtained using real-time 3D transesophageal echocardiography (rt-3DTEE). Volumetric images of the mitral valve at mid-systole may be analyzed by user-initialized segmentation and 3D deformable modeling with continuous medial representation to obtain, a compact representation of shape. The regional leaflet stress distributions may be predicted in normal and diseased (regurgitant) mitral valves using the techniques of the invention.

Semi-automated heart valve morphometry and computational stress analysis from 3D images

A fully automatic method for segmentation of the mitral leaflets in 3D transesophageal echocardiographic (3D TEE) images is provided. The method combines complementary probabilistic segmentation and geometric modeling techniques to generate 3D patient-specific reconstructions of the mitral leaflets and annulus from 3D TEE image data with no user interaction. In the model-based segmentation framework, mitral leaflet geometry is described with 3D continuous medial representation (cm-rep). To capture leaflet geometry in a target 3D TEE image, a pre-defined cm-rep template of the mitral leaflets is deformed such that the negative log of a Bayesian posterior probability is minimized. The likelihood of the objective function is given by a probabilistic segmentation of the mitral leaflets generated by multi-atlas joint label fusion, while the validity constraints and regularization terms imposed by cm-rep act as shape priors that preserve leaflet topology and constrain model fitting.

Fully automatic image segmentation of heart valves using multi-atlas label fusion and deformable medial modeling

A series of rt-3DE images of the mitral valve are quantitatively analyzed so as to enable, for example, prediction of the degree of recurrent ischemic mitral regurgitation (IMR) and comprehensive assessments of leaflet tethering and 'tenting' for the entire mitral valve. In accordance with the method, first, the rt-3DE images are registered with symmetric diffeomorphism to obtain information about how the mitral valve deforms over time. Second, the mitral valve is segmented with the level sets or other known segmentation method at each time point in the cardiac cycle with minimal user interaction. Third, the information about mitral valve structure is reduced into a 3D medial model, a compact representation of shape. In other words, a volumetric segmentation of the mitral valve is condensed to a form that is amenable to clinically relevant morphometry.

Automatic quantification of mitral valve dynamics with real-time 3D ultrasound

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Wynnewood, PA, United States of America

Paul A Yushkevich