ArticlePhysics and imaging in radiation oncology2025
Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning.
Article in Physics and imaging in radiation oncology, 2025. 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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Who cites it
2 citing papers in PubMed.
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- A proof-of-concept study of direct magnetic resonance imaging-based proton dose calculation for brain tumors via neural networks with Monte Carlo-comparable accuracy.Physics and imaging in radiation oncology · 2025Article
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4 authors.
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Abstract
Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences.
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Registered trials
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