ArticleScientific reports2024
Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluating Image Quality of Cone Beam Computed Tomography Using HyperSight® on the Varian TrueBeam.Annals of biomedical engineering · 2026Article
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- 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
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Aligning brain tumor imaging: generating synthetic 3D FA maps from T1-weighted MRI using CycleGAN models.BJR artificial intelligence · 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
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Enhancing pix2pix with Swin Transformer for Cross Modal Brain CT-MR synthesis.Research square · 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
- Semi-supervised action recognition using logit aligned consistency and adaptive negative learning.Scientific reports · 2025Article
- Comparing methods to improve cone-beam computed tomography for dose calculations in adaptive proton therapy.Physics and imaging in radiation oncology · 2025Article
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed method for CT image synthesis, it has notable limitations regarding the inadequate capture of global features. To tackle these challenges, we introduce a refined unsupervised learning model called improved vision transformer CycleGAN (IViT-CycleGAN). Firstly, we integrate a U-net framework that builds upon ViT. Next, we augment the feed-forward neural network by incorporating deep convolutional networks. Lastly, we enhance the stability of the model training process by introducing gradient penalty and integrating an additional loss term into the generator loss. The experiment demonstrates from multiple perspectives that our model-generated synthesizing CT(sCT) has significant advantages compared to other unsupervised learning models, thereby validating the clinical applicability and robustness of our model. In future clinical practice, our model has the potential to assist clinical practitioners in formulating precise radiotherapy plans.
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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.