  Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation
harm to human bodies, is now attracting increasing interest in the medical
imaging field. As the image quality is degraded by low dose radiation, LDCT
exams require specialized reconstruction methods or denoising algorithms.
However, most of the recent effective methods overlook the inner-structure of
the original projection data (sinogram) which limits their denoising ability.
The inner-structure of the sinogram represents special characteristics of the
data in the sinogram domain. By maintaining this structure while denoising, the
noise can be obviously restrained. Therefore, we propose an LDCT denoising
network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise
by utilizing the inner-structure in the sinogram domain. Specifically, we study
the CT imaging mechanism and statistical characteristics of sinogram to design
the sinogram inner-structure loss including the global and local
inner-structure for restoring high-quality CT images. Besides, we propose a
sinogram transformer module to better extract sinogram features. The
transformer architecture using a self-attention mechanism can exploit
interrelations between projections of different view angles, which achieves an
outstanding performance in sinogram denoising. Furthermore, in order to improve
the performance in the image domain, we propose the image reconstruction module
to complementarily denoise both in the sinogram and image domain.
