Recovery of the 3-D shape of the left ventricle from echocardiographic images

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Abstract
A computational method is reported which allows the fully automated recovery of the three-dimensional shape of the cardiac left ventricle from a reduced set of apical echo views. Two typically ill-posed problems have been faced: 1) the detection of the left ventricle contours in each view, and 2) the integration of the detected contour points (which form a sparse and partially inconsistent data set) into a single surface representation. The authors' solution to these problems is based on a careful integration of standard computer vision algorithms with neural networks. Boundary detection comprises three steps: edge detection, edge grouping, and edge classification. The first and second steps (which are typical early-vision tasks not involving specific domain-knowledge) have been performed through fast, well-established algorithms of computer vision. The higher level task of left ventricle-edge discrimination, which involves the exploitation of specific knowledge about the left ventricle silhouette, has been performed by feedforward neural networks. Following the most recent results in the field of computer vision, the first step in solving the problem of recovering the ventricle surface has been the adoption of a physically inspired model of it. Basically, the authors have modeled the left ventricle surface as a closed, thin, elastic surface and the data as a set of radial springs acting on it. The recovery process is equivalent to the settling of the surface-plus-springs system into a stable configuration of minimum potential energy. The finite element discretization of this model leads directly to an analog neural-network implementation. The efficiency of such an implementation has been remarkably enhanced through a learning algorithm which embeds specific knowledge about the shape of the left ventricle in the network. Experiments using clinical echographic sequences are described. Four apical views (each with a different rotation of the probe) have been acquired during a heartbeat from a set of seven normal subjects. These images have been utilized to set the various processing modules and test their capabilities.

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