Dual-Domain Classification-Aided High-Quality PET Synthesis With Shared Information Maximization

Positron emission tomography (PET) is widely applied in clinic for providing crucial diagnosis information. However, its inherent radiation exposure inevitably brings potential health risk for patient. To reduce radiation risk while also obtaining high-quality PET image, we plan to synthesize standard-dose PET (SPET) from low-dose PET (LPET). Since PET images can be represented in both projection domain and image domain (dual domains) emphasizing different information, considering dual domains in PET synthesis could contribute to better performance. In this way, we propose a novel dual-domain model for high-quality PET synthesis, named DCBi-GAN, by introducing a denoising network for the projection domain and an enhancing network for the image domain to effectively exploit dual-domain information. Concretely, the denoising network takes the LPET sinogram converted from LPET image to suppress noise and artifacts in the projection domain. Then, the enhancing network in the image domain takes the denoised LPET image (transferred back from the denoised sinogram) to enhance image quality. Notably, as LPET and SPET images come from the same subject, the abundant shared information between LPET and SPET can be used for boosting synthesis performance. Specially, we design a bi-directional contrastive generative adversarial network (GAN) to encourage maximal preservation of the shared information. Besides, we introduce a mild cognitive impairment (MCI) classification task to enhance clinical applicability of the synthesized PET. Evaluation on both Real Human Brain dataset and Phantom Brain dataset demonstrates effectiveness and superiority of our proposed model.Note to Practitioners—Positron emission tomography (PET) is a primary nuclear imaging technique for tumor detection and brain disorder diagnosis in the early stage of diseases, while the inherent radiation exposure inevitably raises concerns about potential health risk. This article proposes a novel PET image synthesis model to obtain clinically accepted PET image at low dose, namely DCBi-GAN, by taking account of the complementary multi-domain information and the modality shared content information, with a mild cognitive impairment (MCI) classification task to further boost clinical applicability of synthesized PET images. We experimentally validate the effectiveness of proposed DCBi-GAN on two datasets. Our proposed method could facilitate diagnosis and treatment of disease, to be used in the existing computer-aided medical systems.