Anatomy-based PET image reconstruction using nonlocal regularization

Abstract

We propose a new nonlocal regularization method for PET image reconstruction with the aid of high-resolution anatomical images. Unlike conventional reconstruction methods using prior anatomical information, our method using nonlocal regularization does not require additional processes to extract anatomical boundaries or segmented regions. The nonlocal regularization method applied to anatomy-based PET image reconstruction is expected to effectively reduce the error that often occurs due to signal mismatch between the PET image and the anatomical image. We also show that our method can be useful for enhancing the image resolution. To reconstruct the high-resolution image that represents the original underlying source distribution effectively sampled at a higher spatial sampling rate, we model the underlying PET image on a higher-resolution grid and perform our nonlocal regularization method with the aid of the side information obtained from high-resolution anatomical images. Our experimental results demonstrate that, compared to the conventional method based on local smoothing, our nonlocal regularization method enhances the resolution as well as the reconstruction accuracy even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.