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license: mit
tags:
- medical-imaging
- computed-tomography
- image-registration
- deformable-registration
- deformation-synthesis
- variational-autoencoder
- pytorch
- monai
---
# SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
RCTSynth learns a patient-specific distribution of plausible anatomical
deformations from longitudinal thoracic CT images. Given a single moving CT
volume, the model samples an anatomy-conditioned latent code, decodes it into a
stationary velocity field, integrates it into a dense displacement field, and
warps the input to produce a synthetic repeat CT.
The model uses a conditional variational registration architecture with a
full-volume anatomy-conditioned prior and a memory-aware slab decoder. It
supports patient-specific deformation sampling, latent-space traversal, and
the export of warped CT volumes, stationary velocity fields, and displacement
fields.
This repository contains the official `rctsynth.ckpt` weights. The checkpoint
was trained on paired thoracic 4DCT images from the
[DIR-Lab 4DCT dataset](https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/index.html),
with case ID `0` reserved for validation. The training data are not
redistributed here and remain subject to the dataset provider's terms.
The complete implementation, preprocessing instructions, inference notebook,
and configuration are available in the
[RCTSynth GitHub repository](https://github.com/TomasGuija/RCTSynth).
The checkpoint is intended for research and has not been validated for clinical
decision-making, diagnosis, treatment planning, or dose calculation.
## License
The RCTSynth model weights are released under the
[MIT License](https://opensource.org/license/mit). This license does not grant
rights to the DIR-Lab training data or other third-party datasets.
## Citation
Coming soon.
## References
1. Castillo, E., Castillo, R., Martinez, J., Shenoy, M., & Guerrero, T. (2010).
**Four-dimensional deformable image registration using trajectory
modeling.** *Physics in Medicine and Biology, 55*(1), 305–327.
[https://doi.org/10.1088/0031-9155/55/1/018](https://doi.org/10.1088/0031-9155/55/1/018).
2. Castillo, R., Castillo, E., Guerra, R., Johnson, V. E., McPhail, T., Garg,
A. K., & Guerrero, T. (2009). **A framework for evaluation of deformable
image registration spatial accuracy using large landmark point sets.**
*Physics in Medicine and Biology, 54*(7), 1849–1870.
[https://doi.org/10.1088/0031-9155/54/7/001](https://doi.org/10.1088/0031-9155/54/7/001).
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