DAEGAN: Generative adversarial network based on dual-domain attention-enhanced encoder-decoder for low-dose PET imaging

Background: Positron emission tomography (PET) is a valuable medical imaging modality utilized in both clinical and preclinical settings. There is a growing concern regarding the potential radiation exposure associated with the administration of radiotracers. This concern has led to a focus on improving the quality of PET images obtained from low-dose radiotracer injections. Methods: In this study, we propose a novel generative adversarial network (GAN) architecture called dual-domain attention-enhanced encoder-decoder GAN (DAEGAN) for low-dose PET imaging. The DAEGAN architecture incorporates a dual-domain encoder-decoder-based generator, which enables the network to focus simultaneously on the structure information and content features. Additionally, attention modules are integrated into the discriminator and the generator to effectively aggregate meaningful features. A significant contribution of this study is the incorporation of the classification activation map (CAM) loss as a component of the generator loss function for the denoising task. Experiments: Experiments on datasets of small animal and human brain PET scans including normal-dose images and low-dose images for the proposed method and other published methods were carried out. A cross-dataset validation based on a homemade Derenzo phantom was also implemented. Results: In the qualitative evaluation experiments, the proposed model can generate denoised images approaching normal-dose images with better visual structural details. Meanwhile, the proposed method achieved the best score in metrics on the test datasets and cross-dataset validation. Conclusions: The results suggest that the proposed DAEGAN architecture is a promising approach for improving the quality of low-dose PET images compared to state-of-the-art methods.