SCFormer: Spatial and Channel-wise Transformer with Contrastive Learning for High-Quality PET Image Reconstruction

Positron emission tomography (PET) is an extensively employed nuclear medical imaging technique for brain disorder diagnosis, while its intrinsic tracer radiation presents inevitable health risks to patients. To obtain high-quality PET images while mitigating radiation exposure, various convolutional neural network (CNN)-based approaches have been developed to reconstruct standard-dose PET (SPET) images from their corresponding low-dose PET (LPET) counterparts. However, many of these approaches encounter challenges in capturing global semantic information due to the limited receptive field of the convolution operator. Recently, taking advantage of the strong capability of the self-attention mechanism in capturing long-range dependencies, transformer-based methods have demonstrated promising performance. Nevertheless, conventional self-attention exhibits quadratic complexity concerning input size, impeding its broader application in clinical practice. To this end, we propose SCFormer, a transformer-based framework with two novel self-attention mechanisms and a contrastive learning strategy to reconstruct high-quality PET images. Specifically, we propose a novel spatial self-attention (S-SA) and a channel self-attention (C-SA) scheme to achieve a trade-off between reducing model complexity and capturing global information. The S-SA module is capable of modeling spatial relationships between pixels while reducing the complexity of the model. The C-SA module is designed to capture relationships between feature maps, thus exploiting global image information. Moreover, we introduce a contrastive learning strategy to leverage the similarity information between PET image slices, enhancing the reliability of clinical information in the reconstructed images. Validations on two public datasets indicate that our approach surpasses the latest PET reconstruction methods.