Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that correspond to an anatomy of interest is challenging. Previous methods aimed at generating vessel trees mostly in an autoregressive style and could not be applied to vessel graphs with cycles such as capillaries or specific anatomical structures such as the Circle of Willis. Addressing this gap, we introduce the first application of \textit{denoising diffusion models} in 3D vessel graph generation. Our contributions include a novel, two-stage generation method that sequentially denoises node coordinates and edges. We experiment with two real-world vessel datasets, consisting of microscopic capillaries and major cerebral vessels, and demonstrate the generalizability of our method for producing diverse, novel, and anatomically plausible vessel graphs.

翻译：以三维图形式表示的血管网络有助于预测疾病生物标志物、模拟血流并辅助合成图像生成，在临床与临床前场景中均具有重要价值。然而，生成与目标解剖结构对应的逼真血管图仍具挑战性。现有方法多以自回归方式生成血管树，无法适用于含循环结构（如毛细血管）或特定解剖构造（如Willis环）的血管图。为填补这一空白，我们首次将\textit{去噪扩散模型}应用于三维血管图生成领域。本研究的创新点包括一种新颖的两阶段生成方法，该方法依次对节点坐标与边进行去噪。我们在两个真实血管数据集（包含微观毛细血管与主要脑血管）上进行实验，验证了所提方法在生成多样化、新颖且解剖学合理的血管图方面具有普适性。