Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable
  Diffusion Models
Ayman Abaid Muhammad Ali Farooq Niamh Hynes Peter Corcoran Ihsan Ullah
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.