CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis

Jiayi Wang; Hadrien Reynaud; Franciskus Xaverius Erick; Bernhard Kainz

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arXiv · 2508.12900

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis

Jiayi Wang·Hadrien Reynaud·Franciskus Xaverius Erick·Bernhard Kainz

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Abstract

Generative modelling of entire CT volumes conditioned on clinical reports has the potential to accelerate research through data augmentation, privacy-preserving synthesis and reducing regulator-constraints on patient data while preserving diagnostic signals. With the recent release of CT-RATE, a large-scale collection of 3D CT volumes paired with their respective clinical reports, training large text-conditioned CT volume generation models has become achievable. In this work, we introduce CTFlow, a 0.5B latent flow matching transformer model, conditioned on clinical reports. We leverage the A-VAE from FLUX to define our latent space, and rely on the CT-Clip text encoder to encode the clinical reports. To generate consistent whole CT volumes while keeping the memory constraints tractable, we rely on a custom autoregressive approach, where the model predicts the first sequence of slices of the volume from text-only, and then relies on the previously generated sequence of slices and the text, to predict the following sequence. We evaluate our results against state-of-the-art generative CT model, and demonstrate the superiority of our approach in terms of temporal coherence, image diversity and text-image alignment, with FID, FVD, IS scores and CLIP score.

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Jiayi Wang, Hadrien Reynaud, Franciskus Xaverius Erick, Bernhard Kainz. 2025-08-18. CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis. https://arxiv.org/abs/2508.12900

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