ArticleRadiation oncology (London, England)2023
Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy.
Article in Radiation oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Factors influencing dose delivery in rectal cancer radiotherapy using CBCT-guided intensity modulated techniques.Radiation and environmental biophysics · 2026Article
- Efficient and rapid generation of high-quality CT images from CBCT using a flow matching model.Medical physics · 2026Article
- Investigation of the impact of image-guided radiotherapy selection on image registration results and non-coding RNAs for cervical cancer patients.Non-coding RNA research · 2026Article
- 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
- Assessing intra- and interfraction motion and its dosimetric impacts on cervical cancer adaptive radiotherapy based on 1.5T MR-Linac.Radiation oncology (London, England) · 2024Article
- Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of the geometric and dosimetric accuracies of deformable image registration of targets and critical organs in prostate CBCT-guided adaptive radiotherapy.Journal of applied clinical medical physics · 2024Article
- Adaptive assessment based on fractional CBCT images for cervical cancer.Journal of applied clinical medical physics · 2024Article
- Evaluation of a novel CBCT conversion method implemented in a treatment planning system.Radiation oncology (London, England) · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAnatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy.
methodsIn this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain Dose
resultsThe global gamma pass rate of Dose
conclusionsFor cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.
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