In recent years, computational fluid dynamics (CFD) has become a valuable tool for investigating hemodynamics in cerebral aneurysms. CFD provides flow-related quantities, which have been shown to have a potential impact on aneurysm growth and risk of rupture. However, the adoption of CFD tools in clinical settings is currently limited by the high computational cost and the engineering expertise required for employing these tools, e.g., for mesh generation, appropriate choice of spatial and temporal resolution, and of boundary conditions. Herein, we address these challenges by introducing a practical and robust methodology, focusing on computational performance and minimizing user interaction through automated parameter selection. We propose a fully automated pipeline that covers the steps from a patient-specific anatomical model to results, based on a fast, graphics processing unit- (GPU-) accelerated CFD solver and a parameter selection methodology. We use a reduced order model to compute the initial estimates of the spatial and temporal resolutions and an iterative approach that further adjusts the resolution during the simulation without user interaction. The pipeline and the solver are validated based on previously published results, and by comparing the results obtained for 20 cerebral aneurysm cases with those generated by a state-of-the-art commercial solver (Ansys CFX, Canonsburg PA). The automatically selected spatial and temporal resolutions lead to results which closely agree with the state-of-the-art, with an average relative difference of only 2%. Due to the GPU-based parallelization, simulations are computationally efficient, with a median computation time of 40 minutes per simulation.

中文翻译：

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脑动脉瘤血流动力学计算的自动化工作流。

近年来，计算流体动力学（CFD）已成为研究脑动脉瘤血液动力学的重要工具。CFD提供了与流量相关的数量，已显示对动脉瘤的生长和破裂风险具有潜在影响。然而，采用CFD工具在临床上目前由和边界条件的高计算成本和使用这些工具，例如，用于网格生成，空间和时间分辨率的适当选择所需的工程专业知识，有限的。在本文中，我们通过引入实用且健壮的方法，着重于计算性能并通过自动参数选择来最大程度地减少用户交互来应对这些挑战。我们提出了一个全自动流程，涵盖从特定患者的解剖模型到结果的步骤，基于快速图形处理单元（GPU）加速的CFD求解器和参数选择方法。我们使用降阶模型来计算空间和时间分辨率的初始估计，并且使用迭代方法进一步在模拟过程中调整分辨率，而无需用户交互。根据先前发布的结果，并通过比较20例脑动脉瘤病例的结果与最先进的商业求解器（Ansys CFX，Canonsburg PA）产生的结果，对管道和求解器进行验证。自动选择的空间和时间分辨率会导致结果与最新技术非常吻合，平均相对差异仅为2％。由于基于GPU的并行化，因此仿真的计算效率很高，