Horace Hines

Automated cardiac pose computation from reconstructed myocardial SPECT images

A method to automatically compute cardiac pose from 3D reconstructed cardiac SPECT images is presented. Cardiac region of interest (ROI) is generated from input volume as the first step. Left ventricle is then segmented from the ROI and myocardial pruning algorithm is applied to remove unnecessary myocardial mass at the top and the bottom. Pruned myocardial binary mask is subjected to a myocardial clusterification-based skeletonization process. Skeleton of the myocardium is cleaned using a novel distance transform and binning-based histogram cleaning algorithm. Blood pool enclosed within the myocardial walls is iteratively segmented to compute the center of mass (COM) of the left ventricle. An ellipsoid fitting algorithm along with the COM is then employed on the myocardial skeleton to determine the cardiac angles. The method achieves a success rate of 94.4% when evaluated on 643 reconstructed volumes from 331 patients.