ArticleIEEE transactions on medical imaging2022
DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 40 citations in OpenAlex.
- A multi-stage deep learning framework for half-detector truncation and metal artifact reduction in CBCT.Medical physics · 2026Article
- Dual-domain transformer-based learned primal-dual reconstruction for PET imaging.Frontiers in nuclear medicine · 2026Article
- Cross-Domain TransNet for sparse-view CT reconstruction.Frontiers in nuclear medicine · 2026Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.Scientific reports · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.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
- DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.IEEE transactions on medical imaging · 2024Article
- Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Proj2Proj: self-supervised low-dose CT reconstruction.PeerJ. Computer science · 2024Article
- FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.Medical image analysis · 2023Article
- Self-trained deep convolutional neural network for noise reduction in CT.Journal of medical imaging (Bellingham, Wash.) · 2023Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic implants, where the image suffers from additional streak artifacts along with further amplified noise levels, thus affecting the medical diagnosis and other CT-related applications. Previous studies mainly focused either on denoising LDCT without considering metallic implants or full-dose CT metal artifact reduction (MAR). Directly applying previous LDCT or MAR approaches to the issue of simultaneous metal artifact reduction and low-dose CT (MARLD) may yield sub-optimal reconstruction results. In this work, we develop a dual-domain under-to-fully-complete progressive restoration network, called DuDoUFNet, for MARLD. Our DuDoUFNet aims to reconstruct images with substantially reduced noise and artifact by progressive sinogram to image domain restoration with a two-stage progressive restoration network design. Our experimental results demonstrate that our method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings.
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