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Quantitative assessment of hemodynamic and structural characteristics of in vivo brain tissue using total diffuse reflectance spectrum measured in a non-contact fashion

机译:使用以非接触方式测量的全漫反射光谱定量评估体内脑组织的血液动力学和结构特征

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摘要

Here we present a new methodology that investigates the intrinsic structural and hemodynamic characteristics of in vivo brain tissue, in a non-contact fashion, and can be easily incorporated in an intra-operative environment. Within this methodology, relative total diffuse reflectance spectra (RTD(λ)) were acquired from targets using a hybrid spectroscopy imaging system. A spectral interpretation algorithm was subsequently applied to RTD(λ) to retrieve optical properties related to the compositional and structural characteristics of each target. Estimation errors of the proposed methodology were computationally evaluated using a Monte Carlo simulation model for photon migration under various conditions. It was discovered that this new methodology could handle moderate noise and achieve very high accuracy, but only if the refractive index of the target is known. The accuracy of the technique was also validated using a series of tissue phantom studies, and consistent and accurate estimates of μs’(λ)/μa(λ) were obtained from all the phantoms tested. Finally, a small-scale animal study was conducted to demonstrate the clinical utility of the reported method, wherein a forepaw stimulation model was utilized to induce transient hemodynamic responses in somatosensory cortices. With this approach, significant stimulation-related changes (p < 0.001) in cortical hemodynamic and structural characteristics were successfully measured.
机译:在这里,我们提出了一种新的方法,可以以非接触方式研究体内脑组织的固有结构和血液动力学特征,并且可以很容易地将其纳入术中环境。在这种方法中,使用混合光谱成像系统从目标获取了相对总漫反射光谱(RTD(λ))。光谱解释算法随后应用于RTD(λ),以检索与每个靶标的组成和结构特征有关的光学特性。使用蒙特卡洛模拟模型对光子在各种条件下的迁移,对所提出方法的估计误差进行了计算评估。发现只有在知道目标的折射率的情况下,这种新方法才能处理适度的噪声并达到很高的精度。该技术的准确性还通过一系列组织体模研究得到了验证,并且从所有测试的体模中获得了一致且准确的μs’(λ)/μa(λ)估计。最后,进行了一项小规模的动物研究,以证明所报道方法的临床实用性,其中前爪刺激模型用于诱导体感皮层中的短暂血流动力学响应。通过这种方法,成功测量了皮质血液动力学和结构特征中与刺激相关的显着变化(p <0.001)。

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