A deep neural network to recover missing data in small animal pet imaging: Comparison between sinogram-and image-domain implementations

Missing areas in PET sinograms and severe image artifacts as a consequence thereof, still gain prominence not only in sparse-ring detector configurations but also in full-ring PET scanners in case of faulty detectors. Empty bins in the projection domain, caused by inter-block gap regions or any failure in the detector blocks may lead to unacceptable image distortions and inaccuracies in quantitative analysis. Deep neural networks have recently attracted enormous attention within the imaging community and are being deployed for various applications, including handling impaired sinograms and removing the streaking artifacts generated by incomplete projection views. Despite the promising results in sparse-view CT reconstruction, the utility of deep-learning-based methods in synthesizing artifact-free PET images in the sparse-crystal setting is poorly explored. Herein, we investigated the feasibility of a modified U-Net to generate artifact-free PET scans in the presence of severe dead regions between adjacent detector blocks on a dedicated high-resolution preclinical PET scanner. The performance of the model was assessed in both projection and image-space. The visual inspection and quantitative analysis seem to indicate that the proposed method is well suited for application on partial-ring PET scanners.