ArticleScientific reports2021
Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients.
Article in Scientific reports, 2021. 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, 54 citations in OpenAlex.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- ART workflow based on CBCT and end-to-end clinical dynamic phantom validation.BMC medical imaging · 2026Article
- Article
- Optimizing QACT Frequency in Proton Therapy: Analysis of Standard Guidelines Across Treatment Sites.International journal of particle therapy · 2025Article
- Super-resolution CBCT on a new generation flat panel imager of a C-arm gantry linear accelerator.Medical physics · 2025Article
- Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.Journal of applied clinical medical physics · 2025Article
- Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer.Scientific reports · 2024Article
- Article
- Rapid unpaired CBCT-based synthetic CT for CBCT-guided adaptive radiotherapy.Journal of applied clinical medical physics · 2023Article
- A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires.Scientific reports · 2023Article
- CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma.Scientific reports · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Reference-free learning-based similarity metric for motion compensation in cone-beam CT.Physics in medicine and biology · 2022Article
- Review
- Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation.Medical physics · 2021Article
- Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, "Cycle-Deblur GAN", combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.
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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.