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This paper introduces a semantically conditioned latent diffusion model (LDM) for synthesizing arterial-phase cerebral digital subtraction angiography (DSA) images, addressing the scarcity of DSA data due to its invasive nature. The LDM is conditioned on text embeddings representing anatomical circulation (anterior/posterior) and C-arm positions, enabling explicit control over the synthesis process. Evaluation by medical experts showed high clinical realism with Likert scores of 3.1-3.3 and a low Fr茅chet inception distance (FID) of 15.27, demonstrating the potential for generating realistic synthetic DSAs for research and training.
Generate realistic synthetic medical images with diffusion models, opening the door for algorithm development and training where real data is scarce.
Digital subtraction angiography (DSA) plays a central role in the diagnosis and treatment of cerebrovascular disease, yet its invasive nature and high acquisition cost severely limit large-scale data collection and public data sharing. Therefore, we developed a semantically conditioned latent diffusion model (LDM) that synthesizes arterial-phase cerebral DSA frames under explicit control of anatomical circulation (anterior vs.\ posterior) and canonical C-arm positions. We curated a large single-centre DSA dataset of 99,349 frames and trained a conditional LDM using text embeddings that encoded anatomy and acquisition geometry. To assess clinical realism, four medical experts, including two neuroradiologists, one neurosurgeon, and one internal medicine expert, systematically rated 400 synthetic DSA images using a 5-grade Likert scale for evaluating proximal large, medium, and small peripheral vessels. The generated images achieved image-wise overall Likert scores ranging from 3.1 to 3.3, with high inter-rater reliability (ICC(2,k) = 0.80--0.87). Distributional similarity to real DSA frames was supported by a low median Fr\'echet inception distance (FID) of 15.27. Our results indicate that semantically controlled LDMs can produce realistic synthetic DSAs suitable for downstream algorithm development, research, and training.
