SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable conditional generative framework for 3D anatomical deformation synthesis. Based on a conditional variational autoencoder, SynthRCT learns a latent deformation space and decodes sampled latent codes into local stationary velocity fields conditioned on an input anatomy. Local fields are assembled into coherent full-volume transformations, enabling memory-scalable generation for large field-of-view CT...
| Authors | Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica |
|---|---|
| Primary category | cs.CV |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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