DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

• URL: http://arxiv.org/abs/2111.10790v1
• Date: Sun, 21 Nov 2021 10:41:07 GMT
• Title: DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
• Authors: Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, and S. Kevin Zhou
• Abstract summary: iodine radiation in the imaging process induces irreversible injury. Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but the cost is too expensive. We propose textbfDual-textbfDomain textbfDuDoTrans to reconstruct CT image with both the enhanced and raw sinograms.
• Score: 13.358197688568463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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