DiffusionTBAD: Rendering CTA images for type B aortic dissection diagnosis.

The success of diffusion models in medical imaging highlights their potential to generate high-quality synthetic datasets that closely resemble real clinical data, addressing limited dataset availability and patient privacy concerns. We present DiffusionTBAD, a novel text-to-image diffusion-based pipeline for synthesizing diagnostically accurate computed tomography angiography (CTA) images of type B aortic dissection (TBAD). Using few-shot learning, DiffusionTBAD fine-tunes a diffusion model guided by textual prompts to capture the distinct features and variability of TBAD cases. The synthetic data are evaluated using quantitative diversity and similarity metrics, as well as downstream task performance. Augmenting real TBAD datasets with synthetic images improved supervised classification accuracy from 67% to 76%, and pre-training on synthetic images increased segmentation DICE scores from 66% to 70%. Additionally, qualitative assessment by eight healthcare professionals confirmed the high visual realism and diagnostic plausibility of the generated images. These results demonstrate that DiffusionTBAD can enhance model performance while reducing reliance on real patient data, enabling privacy-preserving development of medical imaging models.