用语义控制的扩散模型生成逼真脑部造影图像，助力医疗研究与算法训练。
Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis
- 基于解剖结构和设备位置的语义条件控制，生成特定视角的脑部DSA图像。
- 专家评分平均达3.1~3.3分（5分制），与真实图像分布相似度高（FID=15.27）。
- 适合医学影像算法开发、教学培训及数据稀缺场景下的研究应用。
数字减影血管造影（DSA）在脑血管疾病诊断与治疗中至关重要，但其侵入性及高昂获取成本严重限制大规模数据收集与公开共享。为此，我们构建了一个语义条件化的潜在扩散模型（LDM），可在显式控制解剖循环（前循环 vs 后循环）和标准C臂位置条件下合成动脉期脑部DSA帧。我们整理了一个包含99,349帧的单中心大样本DSA数据集，并利用编码解剖与采集几何的文本嵌入训练该条件化LDM。为评估临床真实性，四位医学专家（含两位神经放射科医生、一位神经外科医生和一位内科专家）采用5级李克特量表系统评估400张合成DSA图像，对近端大血管、中等及远端小血管进行评分。生成图像的整体评分介于3.1至3.3之间，且评分者间一致性良好（ICC(2,k) = 0.80–0.87）。分布相似性通过低中位数弗雷切特起始距离（FID）15.27得到验证。结果表明，语义可控的扩散模型可生成适用于下游算法开发、研究与培训的逼真合成DSA图像。
原文摘要 · Abstract (English)
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.
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