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

Abstract

Abstract: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 (R TD (λ)) were acquired from targets using a hybrid spectroscopy imaging system. A spectral interpretation algorithm was subsequently applied to R TD (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 μ s '(λ)/μ a (λ) were obtained from all the phantoms tested. Finally, a smallscale 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.

Wireless, battery-free optoelectronic systems as subdermal implants for local tissue oximetry

“…With this stochastic photon propagation method, the estimated optical power/photoresponse was calculated for different rStO 2 at two wavelengths. The absorption coefficients [μ a (λ)] as a function of rStO 2 are given by the following equation ( 52 , 53 )μafalse(normalλfalse)= ln10⋅true(false[Hbfalse]tMWtrue(rStO2⋅εHbO2false(normalλfalse)+false(1−rStO2false)⋅εHbfalse(normalλfalse)true)+italicWμWfalse(normalλfalse)true)where [Hb] t is the total concentration of hemoglobin, M W is the molecular weight of hemoglobin (64,500 g mol −1 ), W is the water content (%), and μ W (λ) is the absorption coefficient of water. The ε(λ) of HbO 2 and Hb, and μ W (λ) are available from ( 27 ).…”

“…The scattering coefficient [μ s (λ)] at the dominant emission wavelength was calculated using the following equation ( 53 )μsfalse(normalλfalse)=italicatrue(falseλ500true)−italicb1−italicgwhere a is a scaling coefficient, b is the scattering power, and g is the anisotropy factor. For simulation in rodent’s brain ( a = 21.4 cm −1 and b = 1.2), the calculated μ s (540 nm) = 195.2 cm −1 ( g = 0.89) and μ s (625 nm) = 163.2 cm −1 ( g = 0.90).…”

“…With this stochastic photon propagation method, the estimated optical power/photoresponse was calculated for different rStO 2 at two wavelengths. The absorption coefficients [m a (l)] as a function of rStO 2 are given by the following equation (52,53)…”

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

“…The spectral channel was selected because it presents one of the absorption peaks of hemoglobin in the HS image dataset employed in the experiments. Previous works have shown that the hemoglobin concentration absorption peak is normally found between 500–590 nm [ 48 , 49 , 50 ]. As it can be seen in Figure 3 , shows a high contrast between the brain tissue and the blood vessels, but the background (especially rubber ring markers) and the specular glare are indistinguishable from the blood vessels.…”