Edge preserving bowsher prior with nonlocal weighting for 3D spect reconstruction

In this study, we aim at reconstructing single photon emission computed tomography (SPECT) images using a Bayesian framework to incorporate anatomical information from magnetic resonance (MR) as a priori knowledge about the activity distribution. This is achieved using an anatomically-driven Bowsher prior (BP). Standard BP has the potential to obtain similar results as other state-of-the-art prior embedding techniques, although without expensive calculation demands. However, due to the suboptimal selection of similar neighbours on anatomy, BP is not able to preserve edges. In this paper, we propose a modification of the BP, which can maintain discontinuities while still allowing locally smoothing effect on the same tissue type. Furthermore, a non-local algorithm for the weights estimation is included into the modified BP in order to improve the robustness of the method to non-correlated information between activity and anatomy. Quantitative assessment of our new algorithm is performed using co-registered simulated 3D SPECT/MR data, and compared with a regular BP technique. Preliminary results demonstrate considerable improvement of the edge preserving characteristics and hot lesion detection on the overall quality of the reconstructed images.