mlsanityData to 7 Oct 2026

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

arXiv 2111.10790 ↗First posted 21 Nov 2021eess.IV cs.CVcited by 12

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Abstract (arXiv)

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 local feature extraction capability and the sinogram's global characteristics. To overcome the problem, we propose Dual-Domain Transformer (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

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 33 of its 47 references that are in the map. First posted on arXiv 21 Nov 2021.

Builds on

33 of 47 references.

References · 33

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21 more
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 3; 2025: 2.

12 papers.

  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  3. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  4. 2024An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observationscited by 20
  5. 2023Transformers in Healthcare: A Surveycited by 17
  6. 2024C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstructioncited by 15
  7. 2023Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstructioncited by 6
  8. 2023MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Imagescited by 6
  9. 2023Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensemblescited by 4
  10. 2025DeepSparse: A Foundation Model for Sparse-View CBCT Reconstructioncited by 4
  11. 2024LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CTcited by 0
  12. 2025LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstructioncited by 0