The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulations can in theory inform operators to make better decisions regarding treatment approaches and device selection, but are too slow to do so in practice. In this thesis, we investigate if current machine learning based surrogates can accurately emulate these simulations in a step-by-step manner while making them significantly faster. To do this we train three surrogate models on two simulations that involve a simplified aspiration procedure, with varying levels of geometric complexity. Our results show that two of our models accurately predict singular simulation steps and provide substantial speedups, especially when combined with specific data augmentations. However, the models showed a lack of stability when emulating simulations with complex geometries over longer time periods. Overall, this work provides a foundation for future studies to develop stable methods that scale to realistic numerical physics simulations of mechanical thrombectomy.

翻译：缺血性卒中采用机械取栓治疗时，需在极短时间限制下做出困难决策。数值物理模拟理论上可为操作者提供更优的治疗方案和器械选择指导，但其计算速度过慢，难以在临床实践中应用。本文研究当前基于机器学习的替代模型能否在显著加速的同时，以分步方式准确模拟此类仿真过程。为此，我们针对两种涉及简化抽吸操作（几何复杂度各异）的模拟，训练了三个替代模型。结果表明：两个模型能准确预测单个仿真步骤，并实现显著加速（尤其结合特定数据增强方法时）。然而，在模拟复杂几何构型的长期演化过程时，这些模型表现出稳定性不足的缺陷。总体而言，本研究为未来开发适用于机械取栓现实数值物理模拟的稳定方法奠定了良好基础。