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license: apache-2.0
tags:
- medical-imaging
- ct-generation
- flow-matching
- diffusion
- text-to-3d
- auto-regressive
---
# CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
**ICCV 2025 Workshop on Vision-Language Models for 3D Understanding (VLM3D)**
[[Paper]](https://openaccess.thecvf.com/content/ICCV2025W/VLM3D/papers/Wang_CTFlow_Video-Inspired_Latent_Flow_Matching_for_3D_CT_Synthesis_ICCVW_2025_paper.pdf) | [[GitHub]](https://github.com/WongJiayi/CTFlow)
---
## Overview
CTFlow is a **0.5B latent flow matching transformer** for generating entire 3D CT volumes conditioned on clinical reports.
Key ideas:
- Uses the **FLUX A-VAE** as the latent space encoder/decoder
- Encodes clinical reports with the **CT-CLIP text encoder**
- Generates CT volumes **auto-regressively block-by-block**, keeping memory tractable while maintaining temporal coherence across slices
- Trained on **CT-RATE**, a large-scale dataset of 3D CT volumes paired with clinical reports
---
## Checkpoint
This repository contains the pretrained **STDiT-L2** checkpoint (512M parameters, trained for 680,000 steps):
```
checkpoint-680000/
└── denoiser_ema/ ← use this for inference
```
---
## Usage
See the [GitHub repository](https://github.com/WongJiayi/CTFlow) for full installation instructions, training configs, and inference scripts.
**Quick inference:**
```bash
git clone https://github.com/WongJiayi/CTFlow
cd CTFlow
python auto_regressive_generate/main.py \
--config /path/to/config.yaml \
--ckpt /path/to/checkpoint-680000/denoiser_ema \
--embedding /path/to/ct_embedding.pt \
--output output_frames/ \
--type full-body
```
---
## Citation
```bibtex
@InProceedings{Wang_2025_ICCVW,
author = {Wang, Jiayi and Reynaud, Hadrien and Erick, Franciskus Xaverius and Kainz, Bernhard},
title = {CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
year = {2025},
}
```
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