GPU accelerated rotation-based emission tomography reconstruction

Abstract

Stochastic methods based on Maximum Likelihood Estimation (MLE) provide accurate tomographic reconstruction for emission imaging. Moreover methods based on MLE allow to include an accurate physical model of the imaging setup in the reconstruction process, thus enabling quantitative reconstruction of radio-tracer activity distribution. It has been shown that inclusion of a spatially dependent PSF that models dependence of the CDR with distance from the detector, improves the quality of reconstruction in terms of noise and bias. The computational complexity associated with stochastic methods has limited adoption of such algorithms for clinical use and inclusion of the PSF further increases the computational cost. This work proposes an accelerated implementation of a reconstruction algorithm specifically designed to take advantage of the architecture of a General Purpose Graphics Processing Unit (GPGPU).