The advancement of ischaemic stroke treatment relies on resource-intensive experiments and clinical trials. In order to improve ischaemic stroke treatments, such as thrombolysis and thrombectomy, we target the development of computational tools for in silico trials which can partially replace these animal and human experiments with fast simulations. This study proposes a model that will serve as part of a predictive unit within an in silico clinical trial estimating patient outcome as a function of treatment. In particular, the present work aims at the development and evaluation of an organ-scale microcirculation model of the human brain for perfusion prediction. The model relies on a three-compartment porous continuum approach. Firstly, a fast and robust method is established to compute the anisotropic permeability tensors representing arterioles and venules. Secondly, vessel encoded arterial spin labelling magnetic resonance imaging and clustering are employed to create an anatomically accurate mapping between the microcirculation and large arteries by identifying superficial perfusion territories. Thirdly, the parameter space of the problem is reduced by analysing the governing equations and experimental data. Fourthly, a parameter optimization is conducted. Finally, simulations are performed with the tuned model to obtain perfusion maps corresponding to an open and an occluded (ischaemic stroke) scenario. The perfusion map in the occluded vessel scenario shows promising qualitative agreement with computed tomography images of a patient with ischaemic stroke caused by large vessel occlusion. The results highlight that in the case of vessel occlusion (i) identifying perfusion territories is essential to capture the location and extent of underperfused regions and (ii) anisotropic permeability tensors are required to give quantitatively realistic estimation of perfusion change. In the future, the model will be thoroughly validated against experiments.

缺血性中风治疗的进步依赖于资源密集型实验和临床试验。为了改善缺血性中风治疗，例如溶栓和血栓切除术，我们的目标是开发用于计算机试验的计算工具，该工具可以用快速模拟部分替代这些动物和人体实验。本研究提出了一个模型，该模型将作为计算机内预测单元的一部分临床试验估计患者结果作为治疗的函数。特别是，目前的工作旨在开发和评估用于灌注预测的人脑器官尺度微循环模型。该模型依赖于三室多孔连续体方法。首先，建立了一种快速且稳健的方法来计算代表小动脉和小静脉的各向异性渗透率张量。其次，血管编码动脉自旋标记磁共振成像和聚类用于通过识别表面灌注区域来创建微循环和大动脉之间的解剖学精确映射。第三，通过分析控制方程和实验数据，缩小问题的参数空间。第四，进行参数优化。最后，使用调谐模型执行模拟以获得对应于开放和闭塞（缺血性中风）情况的灌注图。闭塞血管场景中的灌注图显示与由大血管闭塞引起的缺血性中风患者的计算机断层扫描图像有希望的定性一致性。结果强调，在血管闭塞的情况下（i）识别灌注区域对于捕获灌注不足区域的位置和范围至关重要，并且（ii）各向异性渗透率张量需要对灌注变化进行定量现实估计。将来，该模型将通过实验进行彻底验证。闭塞血管场景中的灌注图显示与由大血管闭塞引起的缺血性中风患者的计算机断层扫描图像有希望的定性一致性。结果强调，在血管闭塞的情况下（i）识别灌注区域对于捕获灌注不足区域的位置和范围至关重要，并且（ii）各向异性渗透率张量需要对灌注变化进行定量现实估计。将来，该模型将通过实验进行彻底验证。闭塞血管场景中的灌注图显示与由大血管闭塞引起的缺血性中风患者的计算机断层扫描图像有希望的定性一致性。结果强调，在血管闭塞的情况下（i）识别灌注区域对于捕获灌注不足区域的位置和范围至关重要，并且（ii）各向异性渗透率张量需要对灌注变化进行定量现实估计。将来，该模型将通过实验进行彻底验证。结果强调，在血管闭塞的情况下（i）识别灌注区域对于捕获灌注不足区域的位置和范围至关重要，并且（ii）各向异性渗透率张量需要对灌注变化进行定量现实估计。将来，该模型将通过实验进行彻底验证。结果强调，在血管闭塞的情况下（i）识别灌注区域对于捕获灌注不足区域的位置和范围至关重要，并且（ii）各向异性渗透率张量需要对灌注变化进行定量现实估计。将来，该模型将通过实验进行彻底验证。