ArticleBioengineering (Basel, Switzerland)2019
Deep Encoder-Decoder Adversarial Reconstruction(DEAR) Network for 3D CT from Few-View Data.
Article in Bioengineering (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Integrating CT image reconstruction, segmentation, and large language models for enhanced diagnostic insight.Medical & biological engineering & computing · 2026Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism.Journal of imaging informatics in medicine · 2024Article
- Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network.IEEE transactions on medical imaging · 2023Article
- Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities.Computational and mathematical methods in medicine · 2022Review
- Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).Medical physics · 2021Article
- Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction.IEEE access : practical innovations, open solutions · 2020Article
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3 authors.
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Abstract
X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizingX-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dosehas been an important topic in recent years. Few-view CT image reconstruction is one of the mainways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper,we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT imagereconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in3D instead of 2D geometry, a 3D deep network has a great potential for improving the image qualityin a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publiclyavailable abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to producepromising reconstruction results.
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