ArticlePhysics and imaging in radiation oncology2024
Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images.
Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Deep learning based super-resolution for CBCT dose reduction in radiotherapy.Medical physics · 2025Article
- Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients.Sensors (Basel, Switzerland) · 2024Article
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7 authors.
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Abstract
Background and purpose: Daily cone-beam computed tomography (CBCT) in image-guided radiotherapy administers radiation exposure and subjects patients to secondary cancer risk. Reducing imaging dose remains challenging as image quality deteriorates. We investigated three imaging dose levels by reducing projections and correcting images using two deep learning algorithms, aiming at identifying the lowest achievable imaging dose. Materials and methods: CBCTs were reconstructed with 100%, 25%, 15% and 10% projections. Models were trained (30), validated (3) and tested (8) with prostate cancer patient datasets. We optimized and compared the performance of 1) a cycle generative adversarial network (cycleGAN) with residual connection and 2) a contrastive unpaired translation network (CUT) to generate synthetic computed tomography (sCT) from reduced imaging dose CBCTs. Volumetric modulated arc therapy plans were optimized on a reference intensity-corrected full dose CBCT Results: All sCTs achieved average mean absolute mean absolute error/structural similarity index measure/peak signal-to-noise ratio of Conclusion: sCTs based on different CBCT doses using cycleGAN and CUT were investigated. Based on segmentation accuracy, 25
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