Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics

TL;DR

Aneumo is a large-scale synthetic dataset of intracranial aneurysm hemodynamics, combining real and generated models with detailed flow data to facilitate research into aneurysm mechanisms and prediction.

Contribution

This study introduces a comprehensive dataset of 10,000 synthetic aneurysm models with detailed hemodynamic data, enhancing research capabilities beyond existing datasets.

Findings

Dataset includes 466 real and 9,534 synthetic aneurysm models.

Provides hemodynamic data at eight flow rates, including velocity, pressure, and wall shear stress.

Supports investigation of aneurysm pathogenesis and clinical prediction.

Abstract

Intracranial aneurysm (IA) is a common cerebrovascular disease that is usually asymptomatic but may cause severe subarachnoid hemorrhage (SAH) if ruptured. Although clinical practice is usually based on individual factors and morphological features of the aneurysm, its pathophysiology and hemodynamic mechanisms remain controversial. To address the limitations of current research, this study constructed a comprehensive hemodynamic dataset of intracranial aneurysms. The dataset is based on 466 real aneurysm models, and 10,000 synthetic models were generated by resection and deformation operations, including 466 aneurysm-free models and 9,534 deformed aneurysm models. The dataset also provides medical image-like segmentation mask files to support insightful analysis. In addition, the dataset contains hemodynamic data measured at eight steady-state flow rates (0.001 to 0.004 kg/s), including critical parameters such as flow velocity, pressure, and wall shear stress, providing a valuable resource for investigating aneurysm pathogenesis and clinical prediction. This dataset will help advance the understanding of the pathologic features and hemodynamic mechanisms of intracranial aneurysms and support in-depth research in related fields. Dataset hosted at https://github.com/Xigui-Li/Aneumo.