ArticleIEEE transactions on medical imaging2021
Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 61 citations in OpenAlex.
- Clinical Metadata-Guided Limited-Angle CT Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- Image reconstruction from incomplete data via approximated pseudo-inverse and dual-domain coupled diffusion posterior sampling.Quantitative imaging in medicine and surgery · 2026Article
- Real-time volumetric CBCT reconstruction using surface and X-ray imaging for image-guided radiotherapy.Medical image analysis · 2025Article
- 2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation for Radiation Therapy Using Real Projection Data.IEEE transactions on medical imaging · 2025Article
- Benchmarking deep learning-based low-dose CT image denoising algorithms.Medical physics · 2024Article
- Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation.Medical image analysis · 2024Article
- Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction.Physics in medicine and biology · 2024Article
- FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.Medical image analysis · 2023Article
- Federated Transfer Learning for Low-dose PET Denoising: A Pilot Study with Simulated Heterogeneous Data.IEEE transactions on radiation and plasma medical sciences · 2023Article
- SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data.IEEE transactions on instrumentation and measurement · 2023Article
- DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.Medical physics · 2023Article
- DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.IEEE transactions on medical imaging · 2022Article
- DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.Medical image analysis · 2022Article
- MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET.IEEE transactions on medical imaging · 2021Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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Abstract
Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.
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