ArticleIEEE transactions on medical imaging2019
Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 164 citations in OpenAlex.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Unsupervised deep learning for inverse problems in computed tomography.BMC medical imaging · 2026Article
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.Radiographics : a review publication of the Radiological Society of North America, Inc · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction.Bioengineering (Basel, Switzerland) · 2024Article
- [Reconstruction from CT truncated data based on dual-domain transformer coupled feature learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.Medical physics · 2024Article
- Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions.Quantitative imaging in medicine and surgery · 2024Article
- Self-trained deep convolutional neural network for noise reduction in CT.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy.Medical physics · 2023Article
- Linearized Analysis of Noise and Resolution for DL-Based Image Generation.IEEE transactions on medical imaging · 2023Article
- Attention-based dual-branch deep network for sparse-view computed tomography image reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.IEEE signal processing magazine · 2023Article
- Deep learning tomographic reconstruction through hierarchical decomposition of domain transforms.Visual computing for industry, biomedicine, and art · 2022Article
- Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.Ultrasound in medicine & biology · 2022Article
- A minimum SNR criterion for computed tomography object detection in the projection domain.Medical physics · 2022Article
- A geometry-guided multi-beamlet deep learning technique for CT reconstruction.Biomedical physics & engineering express · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have discovered many solutions to accurately reconstruct the image. However, if these conditions are violated, accurate image reconstruction from line integrals remains an intellectual challenge. In this paper, a deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy. Particularly, accurate reconstructions were achieved for the case when the sparse view reconstruction problem (i.e., compressed sensing problem) is entangled with the classical interior tomographic problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.