目的:对自发性椎动脉夹层动脉瘤(SVADA)的病理生理学了解甚少。我们的目标是使用计算流体动力学(CFD)和深度学习算法研究导致其形成的血液动力学因素。
方法:我们开发了可以使用患者图像作为输入来重建椎基底动脉系统的软件,有和没有SVADA,我们用了三个病人。为了获得动脉瘤形成前后的运动学血流数据,我们利用数值方法求解复杂的Navier-Stokes偏微分方程。这是通过应用有限体积求解器(OpenFoam/HelyxOS)来实现的。此外,我们训练了一个神经常微分方程(NODE)来学习和复制从计算流体动力学(CFD)模拟中获得的动态流线。
结果:在所有三种情况下,我们观察到整个血管内血压分布的平衡,在特定的垂直水平,准确预测未来的SVADA位置。在这两种情况下,有一个占优势的VA,夹层发生在优势动脉,与对侧相比,优势动脉的血压较低。SVADA囊的特征在于壁剪切应力(WSS)降低,并且与湍流增加有关的速度幅度降低。SVADA边界处的高WSS梯度的存在可以解释其扩展。通过能够捕获系统动力学的神经常微分方程(NODE)来学习由CFD生成的流线,以在动脉瘤形成时输出对流动矢量场的有意义的预测。
结论:在我们的系列中,未来SVADA部位及其近端椎基底动脉血压分布的不对称性可准确预测其在所有患者中的位置。深度学习算法可以被训练来模拟生物系统内的血液流动模式。提供计算密集型CFD的替代方案。该技术具有在临床环境中找到实际应用的潜力。
OBJECTIVE: The pathophysiology of spontaneous vertebral artery dissecting aneurysms (SVADA) is poorly understood. Our goal is to investigate the hemodynamic factors contributing to their formation using computational fluid dynamics (CFD) and deep learning algorithms.
METHODS: We have developed software that can use patient imagery as input to recreate the vertebrobasilar arterial system, both with and without SVADA, which we used in a series of three patients. To obtain the kinematic blood flow data before and after the aneurysm forms, we utilized numerical methods to solve the complex Navier-Stokes partial differential equations. This was accomplished through the application of a finite volume solver (OpenFoam/Helyx OS). Additionally, we trained a neural ordinary differential equation (NODE) to learn and replicate the dynamical streamlines obtained from the computational fluid dynamics (CFD) simulations.
RESULTS: In all three cases, we observed that the equilibrium of blood pressure distributions across the VAs, at a specific vertical level, accurately predicted the future SVADA location. In the two cases where there was a dominant VA, the dissection occurred on the dominant artery where blood pressure was lower compared to the contralateral side. The SVADA sac was characterized by reduced wall shear stress (WSS) and decreased velocity magnitude related to increased turbulence. The presence of a high WSS gradient at the boundary of the SVADA may explain its extension. Streamlines generated by CFD were learned with a neural ordinary differential equation (NODE) capable of capturing the system\'s dynamics to output meaningful predictions of the flow vector field upon aneurysm formation.
CONCLUSIONS: In our series, asymmetry in the vertebrobasilar blood pressure distributions at and proximal to the site of the future SVADA accurately predicted its location in all patients. Deep learning algorithms can be trained to model blood flow patterns within biological systems, offering an alternative to the computationally intensive CFD. This technology has the potential to find practical applications in clinical settings.