ArticleCancers2023
CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- 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
- Multispectral PCCT and CBCT imaging for high precision radiotherapy through translation of imaging parameters with machine learning validation.Scientific reports · 2026Article
- Voxel-Based Dose-Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective.Journal of clinical medicine · 2025Article
- A Balanced Multimodal Multi-Task Deep Learning Framework for Robust Patient-Specific Quality Assurance.Diagnostics (Basel, Switzerland) · 2025Article
- [Advances in multimodal biomedical imaging of small animals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation.Scientific reports · 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
- 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
- ACSwinNet: A Deep Learning-Based Rigid Registration Method for Head-Neck CT-CBCT Images in Image-Guided Radiotherapy.Sensors (Basel, Switzerland) · 2024Article
- Article
- Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.BMC cancer · 2023Article
- A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.Physics in medicine and biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient's anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.
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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.