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Position emission tomography (PET) is widely used in clinics and research dueto its quantitative merits and high sensitivity, but suffers from lowsignal-to-noise ratio (SNR). Recently convolutional neural networks (CNNs) havebeen widely used to improve PET image quality. Though successful and efficientin local feature extraction, CNN cannot capture long-range dependencies welldue to its limited receptive field. Global multi-head self-attention (MSA) is apopular approach to capture long-range information. However, the calculation ofglobal MSA for 3D images has high computational costs. In this work, weproposed an efficient spatial and channel-wise encoder-decoder transformer,Spach Transformer, that can leverage spatial and channel information based onlocal and global MSAs. Experiments based on datasets of different PET tracers,i.e., $^{18}$F-FDG, $^{18}$F-ACBC, $^{18}$F-DCFPyL, and $^{68}$Ga-DOTATATE,were conducted to evaluate the proposed framework. Quantitative results showthat the proposed Spach Transformer framework outperforms state-of-the-art deeplearning architectures. Our codes are available athttps://github.com/sijang/SpachTransformer

Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising