Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index (CBFi) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBFi changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

中文翻译：

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使用多层蒙特卡罗建模提高存在全身生理串扰时脑血流量化的准确性

意义：由于全身生理学造成的脑血流量 (CBF) 扩散相关光谱 (DCS) 测量的污染仍然是 DCS 用于神经监测的临床转化的重大挑战。可调谐的多层基于蒙特卡洛 (MC) 的光传输模型有可能消除脑血流指数 (CBFi) 估计中的脑外流串扰。目的：我们探索 MC DCS 模型在高碳酸血症操作期间存在强烈全身生理变化的情况下恢复准确 CBFi 变化的有效性。方法：使用多层板和类似头部的逼真（弯曲）几何形状来运行光子通过头部传播的 MC 模拟。模拟数据被后处理成具有可变脑外厚度的模型，并用于拟合 DCS 多距离强度自相关测量以估计 CBFi 时间过程。将一组人类受试者高碳酸血症治疗的 MC CBFi 值的结果与使用该领域常用的半无限分析模型估计的 CBFi 值进行比较。结果：组平均值表明，与预期的快速 CBF 反应相比，在不同的时间曲线后血流逐渐系统性增加。优化的 MC 模型在几个内在标准和压力调制操作的指导下，能够比假设均匀介质的分析拟合更有效地将 CBFi 变化与头皮血流影响区分开来。三层模型比两层模型表现更好；尽管弯曲的几何形状更接近生理层厚度，但平板和弯曲模型取得了大致相似的结果。结论：三层可调节 MC 模型可用于区分头皮和脑血流的明显变化。压力调制以及生理参数的合理估计可以帮助指导在 MC 模型中选择适当的层厚度。