ArticleMedical physics2020
CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 2 of them syntheses that pooled it.
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Who cites it
104 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Equivariant conditional diffusion model for head and neck CT image synthesis from CBCT.Medical physics · 2026Article
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data.Physics and imaging in radiation oncology · 2026Article
- Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Review
- Auto-segmentation and dose accuracy evaluation of synthetic computed tomography generated from pelvis cone-beam computed tomography images using a cycle-generative adversarial network with a novel loss function.Physics and imaging in radiation oncology · 2026Article
- Evaluation of the efficacy of plan adaptation in stereotactic body proton therapy for pancreatic cancer.Journal of applied clinical medical physics · 2026Article
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Experimental characterization and mitigation of scatter induced spectral contamination in photon counting CBCT for radiotherapy.Medical physics · 2026Article
- Restoration of missing regions in limited field of view computer tomography using an image- and sinogram-based conditional generative adversarial network model.BJR artificial intelligence · 2026Article
- Unsupervised cycle-consistent network for 3D pelvic mono- or multimodal deformable image registration.Radiation oncology (London, England) · 2025Article
- Enhancing CBCT-based CT synthesis using planning MRI in adaptive proton therapy for head cancer: A deep learning approach.Journal of applied clinical medical physics · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Few-shot CBCT-based synthetic CT generation with denoising diffusion probabilistic model.Medical physics · 2025Article
- Article
- The effect of interfractional variation on delivered dose with ultrahypofractionated pencil beam scanning proton therapy for localized prostate cancer.Journal of applied clinical medical physics · 2025Article
- Cone-Beam CT to CT Image Translation Using a Transformer-Based Deep Learning Model for Prostate Cancer Adaptive Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.Journal of imaging informatics in medicine · 2025Article
- Cone-beam computed tomography (CBCT) image-quality improvement using a denoising diffusion probabilistic model conditioned by pseudo-CBCT of pelvic regions.Radiological physics and technology · 2025Article
44 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
purposeCurrent clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy.
methodsThirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison.
resultsAt the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans.
conclusionsThe image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.
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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.