ArticleJournal of medical imaging (Bellingham, Wash.)2019
Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model.Medical physics · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model.ArXiv · 2025Article
- Standardization of scan protocols for RT CT simulator from different vendors using quantitative image quality technique.Journal of applied clinical medical physics · 2024Article
- Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy.Physics in medicine and biology · 2023Article
- Review
- Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.Physics in medicine and biology · 2022Article
- Principles and Perspectives of Radiographic Imaging with Muons.Journal of imaging · 2021Review
- Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps.Diagnostics (Basel, Switzerland) · 2021Article
- A review of deep learning based methods for medical image multi-organ segmentation.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) · 2021Review
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Deep learning in medical image registration: a review.Physics in medicine and biology · 2020Review
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.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) · 2020Review
- CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.Medical physics · 2020Article
- High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.Biomedical physics & engineering express · 2020Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Low-dose computed tomography (CT) is desirable for treatment planning and simulation in radiation therapy. Multiple rescanning and replanning during the treatment course with a smaller amount of dose than a single conventional full-dose CT simulation is a crucial step in adaptive radiation therapy. We developed a machine learning-based method to improve image quality of low-dose CT for radiation therapy treatment simulation. We used a residual block concept and a self-attention strategy with a cycle-consistent adversarial network framework. A fully convolution neural network with residual blocks and attention gates (AGs) was used in the generator to enable end-to-end transformation. We have collected CT images from 30 patients treated with frameless brain stereotactic radiosurgery (SRS) for this study. These full-dose images were used to generate projection data, which were then added with noise to simulate the low-mAs scanning scenario. Low-dose CT images were reconstructed from this noise-contaminated projection data and were fed into our network along with the original full-dose CT images for training. The performance of our network was evaluated by quantitatively comparing the high-quality CT images generated by our method with the original full-dose images. When mAs is reduced to 0.5% of the original CT scan, the mean square error of the CT images obtained by our method is
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