ArticleCell reports. Medicine2023
Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.Scientific reports · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.Frontiers in radiology · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Accuracies of four simulation approaches in reproducing motion artifacts and morphometric parameter biases.Magma (New York, N.Y.) · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- [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
- Free-breathing pediatric cardiac dark-blood imaging with reverse double inversion-recovery and single-shot deep learning reconstruction.Quantitative imaging in medicine and surgery · 2025Article
- Comparison ofEJNMMI physics · 2025Article
- Image quality, diagnostic performance of reduced-dose abdominal CT with artificial intelligence model-based iterative reconstruction for colorectal liver metastasis: a prospective cohort study.Quantitative imaging in medicine and surgery · 2025Article
- Automatic segmentation and diameter measurement of deep medullary veins.Magnetic resonance in medicine · 2025Article
- 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
- Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study.AJNR. American journal of neuroradiology · 2024Article
- Imaging Assessment of Radiation Therapy-Related Normal Tissue Injury in Children: A PENTEC Visionary Statement.International journal of radiation oncology, biology, physics · 2024Review
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.BJR artificial intelligence · 2024Review
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22 authors.
Funding
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Abstract
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.
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