| --- |
| 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). |
|
|
