Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints

Positron emission tomography (PET) is an important medical imaging technique, especially for brain and cancer disease diagnosis. Modern PET scanner is usually combined with computed tomography (CT), where CT image is used for anatomical localization, PET attenuation correction, and radiotherapy treatment planning. Considering radiation dose of CT image as well as increasing spatial resolution of PET image, there is a growing demand to synthesize CT image from PET image (without scanning CT) to reduce risk of radiation exposure. However, most existing works perform learning-based image synthesis to construct cross-modality mapping only in the image domain, without considering of the projection domain, leading to potential physical inconsistency. To address this problem, we propose a novel PET-CT synthesis framework by exploiting dual-domain information (i.e., image domain and projection domain). Specifically, we design both image domain network and projection domain network to jointly learn high-dimensional mapping from PET to CT. The image domain and the projection domain can be connected together with a forward projection (FP) and a filtered back projection (FBP). To further help the PET-to-CT synthesis task, we also design a secondary CT-to-PET synthesis task with the same network structure, and combine the two tasks into a bidirectional mapping framework with several closed cycles. More importantly, these cycles can serve as cycle-consistent losses to further help network training for better synthesis performance. Extensive validations on the clinical PET-CT data demonstrate the proposed PET-CT synthesis framework outperforms the state-of-the-art (SOTA) medical image synthesis methods with significant improvements.