SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
Quick summary
arXiv:2609.08627v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2609.08627v1 Announce Type: cross Abstract: 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.
Why it matters
“SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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