ArticleIEEE transactions on medical imaging2021
DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction.
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 35 papers.
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Who cites it
35 citing papers in PubMed.
- Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- TASC-SwinMT: Task-Adaptive Synergistic Cross-Task Swin Multi-Task Framework for CT and MRI Image Interpolation and Segmentation.Tomography (Ann Arbor, Mich.) · 2026Article
- Deep Few-View High-Resolution Photon-Counting CT at Halved Dose for Extremity Imaging.IEEE transactions on medical imaging · 2026Article
- Data-Driven System Matrix Manipulation Enabling Fast Functional Imaging in Tomography.IEEE transactions on medical imaging · 2026Article
- Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Cross-Domain TransNet for sparse-view CT reconstruction.Frontiers in nuclear medicine · 2026Article
- Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.Biomedical engineering letters · 2025Article
- [Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.Scientific reports · 2025Article
- [A sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field- guided projection completion].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models.Bioengineering (Basel, Switzerland) · 2025Article
- 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
- Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction.Radiology. Artificial intelligence · 2024Article
- HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction.Bioengineering (Basel, Switzerland) · 2024Article
- Development of a separability index for task specific characterization of spectral computed tomography.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2024Article
- MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.Quantitative imaging in medicine and surgery · 2024Article
- Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification.Journal of applied clinical medical physics · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.
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