ArticleFrontiers in oncology2021
Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Scaling deep learning for material imaging with a pseudo 3D model for domain transfer.Nature communications · 2025Article
- Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.Radiation oncology (London, England) · 2025Article
- Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.Journal of imaging informatics in medicine · 2025Article
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory.Scientific reports · 2025Article
- Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients.Sensors (Basel, Switzerland) · 2024Article
- Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer.Radiation oncology journal · 2024Article
- Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images.Physics and imaging in radiation oncology · 2024Article
- Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images.Radiation oncology (London, England) · 2024Article
- The quest for early detection of retinal disease: 3D CycleGAN-based translation of optical coherence tomography into confocal microscopy.Biological imaging · 2024Article
- Article
- Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses.Journal of applied clinical medical physics · 2023Article
- CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma.Scientific reports · 2023Article
- The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review.BJR open · 2023Article
- A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images.Radiation oncology (London, England) · 2022Article
- A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN.Frontiers in oncology · 2022Article
- Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.Frontiers in oncology · 2021Article
- Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo propose a synthesis method of pseudo-CT (CT
methodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CT
resultsThe MAE metric values between the CT
conclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.
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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 Socratic graph.