Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model PerfGAT, integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. Moreover, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.

翻译：灌注磁共振成像（pMRI）为肿瘤血管生成提供了有价值的见解，并有望预测肿瘤基因型，从而有益于胶质瘤患者的预后，但目前仍缺乏针对4D pMRI的有效定制模型。本研究首次尝试使用基于图神经网络（GNN）的时空模型PerfGAT对4D pMRI进行建模，通过整合空间信息和时间动力学来预测胶质瘤患者的异柠檬酸脱氢酶（IDH）突变状态。具体而言，我们提出了一种基于边注意力和负图优化的图结构学习方法，以优化时间相关性建模。此外，我们设计了一个双注意力特征融合模块，用于整合时空特征，同时关注肿瘤相关的脑区。进一步地，我们开发了一种针对时空数据定制的类别平衡增强方法，以缓解临床数据集中常见的标签不平衡问题。我们的实验结果表明，所提出的方法优于其他最先进的方法，有望为患者表征有效建模pMRI。