Stable Diffusion (SD) has gained a lot of attention in recent years in the
field of Generative AI thus helping in synthesizing medical imaging data with
distinct features. The aim is to contribute to the ongoing effort focused on
overcoming the limitations of data scarcity and improving the capabilities of
ML algorithms for cardiovascular image processing. Therefore, in this study,
the possibility of generating synthetic cardiac CTA images was explored by
fine-tuning stable diffusion models based on user defined text prompts, using
only limited number of CTA images as input. A comprehensive evaluation of the
synthetic data was conducted by incorporating both quantitative analysis and
qualitative assessment, where a clinician assessed the quality of the generated
data. It has been shown that Cardiac CTA images can be successfully generated
using using Text to Image (T2I) stable diffusion model. The results demonstrate
that the tuned T2I CTA diffusion model was able to generate images with
features that are typically unique to acute type B aortic dissection (TBAD)
medical conditions.

利用稳定扩散模型基于用户定义的文本提示微调，仅通过有限数量的 CTA 图像作为输入，探索生成合成心脏 CTA 图像的可能性，并通过定量分析和定性评估进行了综合评估，结果表明使用 Text to Image (T2I) 稳定扩散模型调优后能够成功生成具有典型急性 B 型主动脉夹层（TBAD）医疗条件独特特征的心脏 CTA 图像。