Neural network for reconstructing the cross sections of coronary arteries from biplane angiograms

In this paper we describe a new approach for approximately reconstructing a 2D binary pattern from its two orthogonal 1D projections, under the constraint that the shape of the reconstructed binary pattern must represent a typical cross section of a partially occluded coronary artery. Our method consists of two parts: classification by a neural network, which selects the basic shape and orientation of the cross section from a low resolution version of the projections; and a heuristic search which reconstructs the cross sectional shape from the high resolution projection data.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.