用机器学习加速中风血栓清除模拟，提升决策效率。
An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke
- 构建三个机器学习代理模型，逐步模拟血栓清除过程。
- 在简单几何下预测准确，速度提升显著，最高达100倍以上。
- 复杂结构长时间模拟不稳定，适合后续研究优化。
缺血性中风的机械血栓清除治疗需在极短时间内做出决策。数值物理仿真理论上可辅助医生选择最佳方案和器械，但实际应用中速度过慢。本文探究当前机器学习代理模型能否以逐步方式准确模拟此类仿真，并实现显著提速。我们在两个简化抽吸流程的仿真上训练了三种代理模型，涵盖不同几何复杂度。结果表明，其中两种模型能准确预测单步仿真结果，速度提升明显，尤其配合特定数据增强后效果更佳。但在复杂几何下长时间模拟时，模型稳定性不足。本研究为未来开发可扩展至真实血栓清除仿真场景的稳定方法奠定了基础。
原文摘要 · Abstract (English)
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.
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