Left ventricle wall motion tracking using curvature properties

This paper presents the complete implementation of the new algorithm for tracking points on the left ventricle (LV) surface from volumetric cardiac images. We define the local surface stretching as an additional motion parameter of nonrigid transformation. Stretching is constant at all points on the surface for homothetic motion, or follows a polynomial function of certain order (linear in our implementation) in conformal motion. The wall deformation and correspondence information between successive frames of LV in a heart cycle are considered important in evaluating heart behavior and improved diagnosis. We utilize small motion assumption between consecutive frames, hypothesize all possible correspondences, and compute curvature changes for each hypothesis. The computed curvature change is then compared with the one predicted by conformal motion assumption for hypotheses evaluation. We demonstrate the improved performance of the new algorithm utilizing conformal motion with linear stretching assumption over constant stretching assumption on simulated data. Then, the algorithm is applied to real cardiac (CT) images and the stretching of the LV wall is determined. The data set used in our experiments was provided by Dr. Eric Hoffman at University of Pennsylvania Medical school and consists of 16 volumetric (128 by 128 by 118) images taken through the heart cycle.