부산대학교 도서관

Training a denoiser for translating low-dose to normal-dose computed tomography (LDCT to NDCT) requires collecting spatially corresponded paired data, yet it is impractical. Existing works aim to ‘add’ noise to real NDCT for synthesizing paired LDCT, but often failed to model the complex noise-structure entanglement, and therefore led to a suboptimal trained denoiser. In this paper, we propose a fresh solution to mimic more reasonable LDCT by modeling the structure-entangled noise patterns. Our motivation is based on a fact that the noise is originally carried by the source sinogram, and then passed to the reconstructed CT and intertwined with the structures via the filtered back-projection (FBP). In light of this, we first convert a NDCT image to the source sinogram by forward projection, and then design a Degrading CT Network (DeCTNet) to learn noise from the origin. Specifically, DeCTNet consists of two sequential networks, i.e., sinogram and reconstruction networks (SinoNet and RecoNet). SinoNet learns to directly add noise to the NDCT-converted sinogram, and RecoNet learns to further process the reconstructed CT image guided by real but unpaired LDCT images. DeCTNet utilizes a differentiable FBP operator to naturally reinvent the sinogram noise to the entangled noise-structure patterns in CT, and thus bridge SinoNet and RecoNet in an end-to-end training. Moreover, adversarial loss and content-fidelity loss are jointly minimized to effectively learn the noise characteristics and content retention in LDCT synthesis. Both quantitative and qualitative evaluations demonstrate that the denoiser trained using DeCTNet-synthesized pairs outperforms those trained using pairs synthesized by other state-of-the-arts, and radiologists are often not able to distinguish our denoised LDCTs from the real NDCTs.