We present a maximum a posteriori (MAP) reconstruction method of increasing the pixel resolution of positron emission tomography (PET) whose typical pixel resolution is relatively low compared to that of other medical imaging modalities. We first model the underlying PET image on a finer grid and downsample it before performing forward projections to process with the observed low-resolution projection data at each iteration. We then apply a prior modeled by a linear combination of local and nonlocal regularizers to our MAP algorithm. The idea of combining the two different types of regularizers is based on our own notion that, while local regularizers are suitable for preserving fine-scale edges, non-local regularizers are suitable for preserving coarsescale edges or flat regions. Our preliminary results show that the proposed method improves the reconstruction accuracy by compromising trade-offs of the two different types of regularization on a finer grid for high-resolution reconstruction.
Xue Ren, Soo-Jin Lee, "High-Resolution Image Reconstruction for PET Using Local and Non-local Regularizations" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XV, 2017, pp 174 - 178, https://doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-443