Extensive studies suggested that fluid mechanical markers of intracranial aneurysms (IAs) derived from Computational Fluid Dynamics (CFD) can indicate disease progression risks, but to date this has not been translated clinically. This is because CFD requires specialized expertise and is time-consuming and low throughput, making it difficult to support clinical trials. A deep learning model that maps IA morphology to biomechanical markers can address this, enabling physicians to obtain these markers in real time without performing CFD. Here, we show that a Graph Transformer model that incorporates temporal information, which is supervised by large CFD data, can accurately predict Wall Shear Stress (WSS) across the cardiac cycle from IA surface meshes. The model effectively captures the temporal variations of the WSS pattern, achieving a Structural Similarity Index (SSIM) of up to 0.981 and a maximum-based relative L2 error of 2.8%. Ablation studies and SOTA comparison confirmed its optimality. Further, as pulsatile CFD data is computationally expensive to generate and sample sizes are limited, we engaged a strategy of injecting a large amount of steady-state CFD data, which are extremely low-cost to generate, as augmentation. This approach enhances network performance substantially when pulsatile CFD data sample size is small. Our study provides a proof of concept that temporal sequences cardiovascular fluid mechanical parameters can be computed in real time using a deep learning model from the geometric mesh, and this is achievable even with small pulsatile CFD sample size. Our approach is likely applicable to other cardiovascular scenarios.

翻译：大量研究表明，通过计算流体动力学（CFD）获得的颅内动脉瘤（IAs）流体力学标志物能够指示疾病进展风险，但迄今为止这一方法尚未实现临床转化。这主要是因为CFD需要专业知识，且耗时较长、通量低，难以支撑临床试验。一种能够将IA形态映射至生物力学标志物的深度学习模型可以解决这一问题，使医生无需执行CFD即可实时获取这些标志物。本文中，我们展示了一种融合时序信息、并由大规模CFD数据监督的图Transformer模型，能够从IA表面网格准确预测整个心动周期内的壁面剪应力（WSS）。该模型有效捕捉了WSS模式的时序变化，结构相似性指数（SSIM）最高可达0.981，基于最大值的相对L2误差为2.8%。消融实验与SOTA对比验证了其最优性。此外，由于脉动CFD数据生成计算成本高且样本量有限，我们采用了一种数据增强策略：注入大量生成成本极低的稳态CFD数据。当脉动CFD数据样本量较小时，该方法显著提升了网络性能。我们的研究提供了概念验证，表明心血管流体力学参数的时序序列可以通过深度学习模型从几何网格中实时计算，即使在脉动CFD样本量较小的情况下也可实现。该方法很可能适用于其他心血管场景。