ArticleDiagnostics (Basel, Switzerland)2021
Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 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
- 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
- Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.Journal of imaging informatics in medicine · 2025Article
- Use of a deep learning neural network to generate bone suppressed images for markerless lung tumor tracking.Medical physics · 2025Article
- Cone-beam computed tomography (CBCT) image-quality improvement using a denoising diffusion probabilistic model conditioned by pseudo-CBCT of pelvic regions.Radiological physics and technology · 2025Article
- Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.Radiation oncology (London, England) · 2025Article
- Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients.Sensors (Basel, Switzerland) · 2024Article
- Image Quality Comparison of Three 3D Mobile X-Ray Imaging Guidance Devices Used in Spine Surgery: A Phantom Study.Sensors (Basel, Switzerland) · 2024Article
- Comparative analysis of delivered and planned doses in target volumes for lung stereotactic ablative radiotherapy.Radiation oncology (London, England) · 2024Article
- A joint learning framework for multisite CBCT-to-CT translation using a hybrid CNN-transformer synthesizer and a registration network.Frontiers in oncology · 2024Article
- Article
- Inter-observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients.Journal of applied clinical medical physics · 2023Article
- Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses.Journal of applied clinical medical physics · 2023Article
- Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography.Physics and imaging in radiation oncology · 2023Article
Corrections and comments
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2 authors.
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Abstract
Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.
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