用图神经网络预测脑血管血流，实现快速精准诊断。
Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets
- 构建图神经网络，基于临床数据预测未知血管结构的血流与压力。
- 在复杂病理血管上实现0.824的流量预测相关性，0.727的压力相关性。
- 适合需要实时评估脑血管疾病的临床医生和研究人员。
准确预测脑血流对脑血管疾病诊疗至关重要。传统计算方法虽精确但耗时长，难以用于实时临床场景。本文提出一种图神经网络（GNN），用于预测未见的脑血管网络结构中的血流与压力，模型基于狭窄患者临床数据训练，涵盖多种入流条件、血管拓扑与连接性，增强泛化能力。在足够训练数据下，压力预测的皮尔逊相关系数达0.727，流量预测达0.824。结果表明该方法在处理复杂病理性血管网络方面具备实时脑血管诊断潜力。
原文摘要 · Abstract (English)
Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) to predict blood flow and pressure in previously unseen cerebral vascular network structures that were not included in training data. The GNN was developed using clinical datasets from patients with stenosis, featuring complex and abnormal vascular geometries. Additionally, the GNN model was trained on data incorporating a wide range of inflow conditions, vessel topologies, and network connectivities to enhance its generalization capability. The approach achieved Pearson's correlation coefficients of 0.727 for pressure and 0.824 for flow rate, with sufficient training data. These findings demonstrate the potential of the GNN for real-time cerebrovascular diagnostics, particularly in handling intricate and pathological vascular networks.
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