Abstract
Deep learning has attracted attention for positron emission tomography (PET) image reconstruction task, however, it remains necessary to further improve the image quality. In this study, we propose a novel CNN-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of coincidence events. We evaluated the proposed method using Monte Carlo simulation data obtained from a digital brain phantom. Compared to the case without it, when using direction information, the peak signal-to-noise ratio and structural similarity were improved by 1.2 dB and 0.02, at a coincidence time resolution of 300 ps. The calculation times of the proposed method were significantly faster than the conventional iterative reconstruction. These results indicate that the proposed method improves both the speed and image quality of TOF-PET image reconstruction.
Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation Maximisation
