Ordered Subsets EM algorithm for PET Image Reconstruction by use of Dictionary Learning and TV Regularization

Abstract

Nowadays, positron emission tomography (PET) scan is focused in the field of disease diagnosis. In order to obtain a clear image in the PET scan, it is necessary to increase the S/N ratio, which leads to an increase in exposure dose at the time of observation. For this reason, it is desired to increase the S/N ratio of the image while suppressing the exposure dose. In this research, we applied a method combining two noise reduction methods to this problem. First, we used a noise reduction method using dictionary learning for the sinogram representation. The second used a noise reduction method for the real image representation based on the regularization approach. As a result, it was found that an approach combining two noise reduction methods is more effective than the conventional method.