Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging

Abstract

The use of accurate system response modeling has been proven to be an essential key of SPECT image reconstruction, with its usage leading to overall improvement of image quality. The aim of this work was to investigate the imaging performance using an XCAT brain perfusion phantom of two modeling strategies, one based on analytic techniques and the other one based on GATE Monte-Carlo simulation. In addition, an efficient forced detection approach to improve the overall simulation efficiency was implemented and its performance was evaluated. We demonstrated that accurate modeling of the system matrix generated by Monte-Carlo simulation for iterative reconstruction leads to superior performance compared to analytic modeling in the case of clinical I-123 brain imaging. It was also shown that the use of the forced detection approach provided a quantitative and qualitative enhancement of the reconstruction.