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.
We present a penalized-likelihood (PL) reconstruction method for transmission tomography where a new type of regularization, namely the weighted median regularization, is used in place of the conventional local smoothing-based regularization. In this work we note that, since the performance of the weighted median regularization is affected by the smoothing parameter that weights the regularization term with respect to the likelihood term, it is challenging to choose an optimal value of the parameter. To overcome this problem, we propose an adaptive method of choosing the smoothing parameter based on the pixel roughness derived from the histogram of a point-wise standard deviation image at each PL iteration. Our experimental results show that the proposed method provides acceptably good reconstructions which are almost comparable to the best reconstructions obtained with manually chosen smoothing parameter.