Comparative study of diverse model building strategies for 3D-ASM segmentation of dynamic gated SPECT data

Over the course of the last two decades, myocardial perfusion with Single Photon Emission Computed Tomography (SPECT) has emerged as an established and well-validated method for assessing myocardial ischemia, viability, and function. Gated-SPECT imaging integrates traditional perfusion information along with global left ventricular function. Despite of these advantages, inherent limitations of SPECT imaging yield a challenging segmentation problem, since an error of only one voxel along the chamber surface may generate a huge difference in volume calculation. In previous works we implemented a 3-D statistical model-based algorithm for Left Ventricle (LV) segmentation of in dynamic perfusion SPECT studies. The present work evaluates the relevance of training a different Active Shape Model (ASM) for each frame of the gated SPECT imaging acquisition in terms of their subsequent segmentation accuracy. Models are subsequently employed to segment the LV cavity of gated SPECT studies of a virtual population. The evaluation is accomplished by comparing point-to-surface (P2S) and volume errors, both against a proper Gold Standard. The dataset comprised 40 voxel phantoms (NCAT, Johns Hopkins, University of of North Carolina). Monte-Carlo simulations were generated with SIMIND (Lund University) and reconstructed to tomographic slices with ASPIRE (University of Michigan).