Surface modeling in heart motion analysis

Chang Wen Chen; Thomas S. Huang

doi:10.1117/12.135158

1 February 1992 Surface modeling in heart motion analysis

Chang Wen Chen, Thomas S. Huang

Proceedings Volume 1610, Curves and Surfaces in Computer Vision and Graphics II; (1992) https://doi.org/10.1117/12.135158
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

This paper presents a new approach for left ventricle surface modeling in the analysis of dynamic behavior of the heart from biplane cineangiograms. We decompose the motion and deformation analysis of the left ventricle into two stages by coarse-to-fine modeling the moving surface of the left ventricle. Such a two-step surface modeling enables us to formulate the complex motion analysis as a series of well-defined parameter estimation algorithms. We model the globally deformable surface by a parametrized family of surfaces known as superquadrics that are able to model expansion/contraction and twisting deformations. The deformation parameters are obtained by fitting the given 3-D data to the superquadric modeling primitives. The residues of such a fitting are the measure of local deformations that global modeling primitives are unable to abstract. These residues are then interpolated by spherical harmonic surface modeling primitives to form a residue surface. Local deformation tensor analysis of the left ventricle is therefore based on the overall surfaces constructed by superposition of local spherical harmonic residue surfaces on top of the global superquadric surfaces. Animations of the estimated dynamic surface are generated using scientific visualization techniques in order to vividly examine the complex spatial and temporal nature of the left ventricle. These animations are consistent with a priori knowledge of the left ventricle and hence show the success of the surface modeling algorithm.

Citation Download Citation

Chang Wen Chen and Thomas S. Huang "Surface modeling in heart motion analysis", Proc. SPIE 1610, Curves and Surfaces in Computer Vision and Graphics II, (1 February 1992); https://doi.org/10.1117/12.135158

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