Nonlocal-means approaches to anatomy-based PET image reconstruction

We propose nonlocal-means (NLM) approaches to incorporating prior anatomical information into PET image reconstruction. In our NLM approaches, adaptive smoothing is performed on the PET image by using the weights that reflect the self-similarity property of the underlying PET image with the aid of the additional information obtained from the anatomical image. Unlike conventional anatomy-based reconstruction methods, our methods using the anatomy-based NLM priors do not require additional processes to extract anatomical boundaries or segmented regions. In this work we apply the NLM algorithm to both the maximum a posteriori (MAP) and the minimum cross entropy (MXE) reconstruction methods. Our experimental results demonstrate that, compared to the conventional methods based on local smoothing, our methods based on the nonlocal means algorithm remarkably improve the reconstruction accuracy in terms of both percentage error and regional bias even with imperfect anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.