College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China.
Yunnan Key Laboratory of Computer Technology Applications, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650504, China.
Sci Data. 2024 Nov 12;11(1):1213. doi: 10.1038/s41597-024-04056-8.
Intracranial aneurysm is a cerebrovascular disease associated with a high rupture risk, often resulting in death or severe disability. Recent advances in AI enable the prediction of intracranial aneurysm initiation, progression, and rupture through medical image analysis. Despite growing research interest, there is a shortage of publicly available datasets for training and validating AI models. This paper presents a comprehensive dataset comprising high-resolution CTA images of 99 patients with 105 MCA aneurysms and 44 normal healthy controls, along with their respective clinical data and 3D models of aneurysms and the parent arteries derived from the CTA images. Furthermore, recognizing the significance of blood hemodynamics on aneurysm development, this dataset also included the morphological and hemodynamic parameters obtained by computational fluid dynamics (CFD) for each patient and healthy control, which can be utilized by researchers without prior CFD experience. This dataset will facilitate hypothesis-driven or data-driven research on intracranial aneurysms, and has the potential to deepen our understanding of this disease.
颅内动脉瘤是一种与高破裂风险相关的脑血管疾病，常导致死亡或严重残疾。人工智能的最新进展使得通过医学图像分析来预测颅内动脉瘤的发生、进展和破裂成为可能。尽管研究兴趣日益浓厚，但用于训练和验证人工智能模型的公开数据集仍然不足。本文提出了一个全面的数据集，包含 99 名患者的高分辨率 CTA 图像，这些患者有 105 个 MCA 动脉瘤和 44 名正常健康对照者，以及他们各自的临床数据和从 CTA 图像中提取的动脉瘤及其母动脉的 3D 模型。此外，认识到血液动力学对动脉瘤发展的重要性，该数据集还包括每个患者和健康对照者的计算流体动力学（CFD）获得的形态和血液动力学参数，这些参数可供没有 CFD 经验的研究人员使用。该数据集将有助于针对颅内动脉瘤进行假设驱动或数据驱动的研究，并有可能加深我们对这种疾病的理解。
