Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain
Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain
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DOI：
10.1364/boe.6.004288
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发表时间：
2015-11-01
影响因子：
3.4
通讯作者：
St Lawrence, Keith
中科院分区：
文献类型：
--
作者：
Verdecchia, Kyle;Diop, Mamadou;St Lawrence, Keith
漫相关光谱是一种非侵入性的光学技术，能够监测组织的血流灌注。由集散控制系统产生的归一化时间强度自相关函数的典型特征是假设红细胞的运动可以被建模为类似布朗扩散的过程，而不是预期的随机流模型。最近，人们提出了一种结合了随机流模型和布朗流模型的混合模型，称为流体动力扩散模型。本研究的目的是探索描述直接在脑上获得的自相关函数的最佳模型，以避免脑外组织的混淆效应。采集了11头猪在正常碳酸血症和低碳酸血症期间的数据，并通过计算机断层扫描(CTP)证实了血流变化。水动力扩散模型对自相关函数的拟合效果最好，而布朗扩散模型和流体动力扩散模型测得的相对流量变化没有显著差异。(C)2015年美国光学学会
Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion. The normalized temporal intensity autocorrelation function generated by DCS is typically characterized by assuming that the movement of erythrocytes can be modeled as a Brownian diffusion-like process instead of by the expected random flow model. Recently, a hybrid model, referred to as the hydrodynamic diffusion model, was proposed, which combines the random and Brownian flow models. The purpose of this study was to investigate the best model to describe autocorrelation functions acquired directly on the brain in order to avoid confounding effects of extracerebral tissues. Data were acquired from 11 pigs during normocapnia and hypocapnia, and flow changes were verified by computed tomography perfusion (CTP). The hydrodynamic diffusion model was found to provide the best fit to the autocorrelation functions; however, no significant difference for relative flow changes measured by the Brownian and hydrodynamic diffusion models was observed. (C) 2015 Optical Society of America