  While Computed Tomography (CT) reconstruction from X-ray sinograms is
necessary for clinical diagnosis, iodine radiation in the imaging process
induces irreversible injury, thereby driving researchers to study sparse-view
CT reconstruction, that is, recovering a high-quality CT image from a sparse
set of sinogram views. Iterative models are proposed to alleviate the appeared
artifacts in sparse-view CT images, but the computation cost is too expensive.
Then deep-learning-based methods have gained prevalence due to the excellent
performances and lower computation. However, these methods ignore the mismatch
between the CNN's \textbf{local} feature extraction capability and the
sinogram's \textbf{global} characteristics. To overcome the problem, we propose
\textbf{Du}al-\textbf{Do}main \textbf{Trans}former (\textbf{DuDoTrans}) to
simultaneously restore informative sinograms via the long-range dependency
modeling capability of Transformer and reconstruct CT image with both the
enhanced and raw sinograms. With such a novel design, reconstruction
performance on the NIH-AAPM dataset and COVID-19 dataset experimentally
confirms the effectiveness and generalizability of DuDoTrans with fewer
involved parameters. Extensive experiments also demonstrate its robustness with
different noise-level scenarios for sparse-view CT reconstruction. The code and
models are publicly available at https://github.com/DuDoTrans/CODE
