Department of Physics, Stanford University School of Humanities and Sciences, Stanford, CA, USA; Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford, CA, USA; Department of Neurosurgery, Stanford, CA, USA.
Comput Biol Med. 2019 Oct;113:103416. doi: 10.1016/j.compbiomed.2019.103416. Epub 2019 Aug 27.
Theoretical modeling allows investigations of cerebral arteriovenous malformation (AVM) hemodynamics, but current models are too simple and not clinically representative. We developed a more realistic AVM model based on graphics processing unit (GPU) computing, to replicate highly variable and complex nidus angioarchitectures with vessel counts in the thousands-orders of magnitude greater than current models.
We constructed a theoretical electrical circuit AVM model with a nidus described by a stochastic block model (SBM) of 57 nodes and an average of 1000 plexiform and fistulous vessels. We sampled and individually simulated 10,000 distinct nidus morphologies from this SBM, constituting an ensemble simulation. We assigned appropriate biophysical values to all model vessels, and known values of mean intravascular pressure (P) to extranidal vessels. We then used network analysis to calculate P and volumetric flow rate within each nidus vessel, and mapped these values onto a graphic representation of the nidus network. We derived an expression for nidus rupture risk and conducted a model parameter sensitivity analysis.
Simulations revealed a total intranidal volumetric blood flow ranging from 268 mL/min to 535 mL/min, with an average of 463 mL/min. The maximum percentage rupture risk among all vessels in the nidus ranged from 0% to 60%, with an average of 29%.
This easy to implement biomathematical AVM model, allowed by parallel data processing using advanced GPU computing, will serve as a useful tool for theoretical investigations of AVM therapies and their hemodynamic sequelae.
理论建模可用于研究脑动静脉畸形（AVM）的血流动力学，但目前的模型过于简单，不具有临床代表性。我们基于图形处理单元（GPU）计算开发了一种更逼真的 AVM 模型，可复制具有数千个血管计数的高度可变且复杂的病灶血管结构，其数量级比当前模型大几个数量级。
我们构建了一个理论电电路 AVM 模型，其中一个病灶由一个具有 57 个节点和平均 1000 个丛状和瘘管血管的随机块模型（SBM）来描述。我们从这个 SBM 中采样并单独模拟了 10000 个不同的病灶形态，构成了一个集合模拟。我们为所有模型血管分配了适当的生物物理值，并为非病灶血管分配了已知的平均血管内压（P）值。然后，我们使用网络分析计算每个病灶血管内的 P 和容积流量，并将这些值映射到病灶网络的图形表示上。我们推导出了一个病灶破裂风险的表达式，并进行了模型参数敏感性分析。
模拟结果显示，病灶内总容积血流范围从 268 毫升/分钟到 535 毫升/分钟，平均为 463 毫升/分钟。病灶内所有血管的最大破裂风险百分比范围从 0%到 60%，平均为 29%。
这种易于实施的生物数学 AVM 模型，通过使用先进的 GPU 计算进行并行数据处理得以实现，将成为理论研究 AVM 治疗及其血流动力学后果的有用工具。
