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 Fréchet 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.

翻译：数字减影血管造影（DSA）在脑血管疾病的诊断和治疗中起着核心作用，但其有创性和高昂的采集成本严重限制了大规模数据收集和公共数据共享。因此，我们开发了一种语义条件的潜在扩散模型（LDM），该模型在解剖循环（前循环与后循环）和标准C型臂位置的显式控制下合成动脉期脑血管DSA帧。我们整理了一个包含99,349帧的大型单中心DSA数据集，并使用编码了解剖结构和采集几何信息的文本嵌入来训练条件LDM。为了评估临床真实性，包括两名神经放射科医生、一名神经外科医生和一名内科专家在内的四位医学专家，使用5级李克特量表对400张合成DSA图像进行了系统评分，以评估近端大血管、中血管和小外周血管。生成的图像获得的图像整体李克特分数在3.1至3.3之间，具有较高的评分者间信度（ICC(2,k) = 0.80--0.87）。与真实DSA帧的分布相似性得到了较低的中位数Fréchet起始距离（FID = 15.27）的支持。我们的结果表明，语义控制的LDM能够生成适用于下游算法开发、研究和训练的逼真合成DSA图像。