A method based on spatial frequency domain imaging platform and different back-processing algorithms are used together to present the refractive index (RI) of mouse brain tissue in the NIR spectral range. Structured light patterns at two frequencies of six wavelengths ranging between 690 and 970 nm were serially projected onto mouse scalp while a camera mounted above the head captures the reflected diffuse light. In the computer, the recorded images at each wavelength were converted to spatial absorption and scattering maps, respectively. Then, algorithms based on Maxell equations, Hilbert Transform, and Kramers-Kronig relations are used separately to calculate the RI. Once the value of RI at each wavelength was obtained, the wavelength dependence of RI was fitted using four well-known dispersion models. In addition, three-dimensional surface-profile distribution of RI was achieved based on phase profilometry principle. During this study, RI was evaluated in mouse model of heatstress (HS) showing a decrease in RI with increasing wavelength and overall differences pre-and-post HS. An in-house system was built to control the body temperature and thermal camera together with IR laser temperature meter gun was used to measure brain temperature. The changes in RI we observed reflect the pathophysiology of the brain during HS and present an additional advantage of spatial frequency domain imaging technique to characterize brain function. Overall, this work demonstrates a proofof- concept of the proposed method which we believe will be beneficial to the Biophotonics' community.
A practical algorithm for estimating the wavelength-dependent refractive index (RI) of a turbid sample in the spatial frequency domain with the aid of Kramers–Kronig (KK) relations is presented. In it, phase-shifted sinusoidal patterns (structured illumination) are serially projected at a high spatial frequency onto the sample surface (mouse scalp) at different near-infrared wavelengths while a camera mounted normally to the sample surface captures the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial absorption maps by logarithmic function, and once the absorption coefficient information is obtained, the imaginary part (k) of the complex RI (CRI), based on Maxell’s equations, can be calculated. Using the data represented by k, the real part of the CRI (n) is then resolved by KK analysis. The wavelength dependence of n ( λ ) is then fitted separately using four standard dispersion models: Cornu, Cauchy, Conrady, and Sellmeier. In addition, three-dimensional surface-profile distribution of n is provided based on phase profilometry principles and a phase-unwrapping-based phase-derivative-variance algorithm. Experimental results demonstrate the capability of the proposed idea for sample’s determination of a biological sample’s RI value.
We used spatially modulated optical imaging system to assess the effect of temperature elevation on intact brain tissue in a mouse heatstress model. Heatstress or heatstroke is a medical emergency defined by abnormally elevated body temperature that causes biochemical, physiological and hematological changes. During experiments, brain temperature was measured concurrently with a thermal camera while core body temperature was monitored with rectal thermocouple probe. Changes in a battery of macroscopic brain physiological parameters, such as hemoglobin oxygen saturation level, cerebral water content, as well as intrinsic tissue optical properties were monitored during temperature elevation. These concurrent changes reflect the pathophysiology of the brain during heatstress and demonstrate successful monitoring of thermoregulation mechanisms. In addition, the variation of tissue refractive index was calculated showing a monotonous decrease with increasing wavelength. We found increased temperature to greatly affect both the scattering properties and refractive index which represent cellular and subcellular swelling indicative of neuronal damage. The overall trends detected in brain tissue parameters were consistent with previous observations using conventional medical devices and optical modalities.
Spectral data enabling the derivation of a biological tissue sample’s complex refractive index (CRI) can provide a range of valuable information in the clinical and research contexts. Specifically, changes in the CRI reflect alterations in tissue morphology and chemical composition, enabling its use as an optical marker during diagnosis and treatment. In the present work, we report a method for estimating the real and imaginary parts of the CRI of a biological sample using Kramers–Kronig (KK) relations in the spatial frequency domain. In this method, phase-shifted sinusoidal patterns at single high spatial frequency are serially projected onto the sample surface at different near-infrared wavelengths while a camera mounted normal to the sample surface acquires the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial phase maps using KK analysis and are then calibrated against phase-models derived from diffusion approximation. The amplitude of the reflected light, together with phase data, is then introduced into Fresnel equations to resolve both real and imaginary segments of the CRI at each wavelength. The technique was validated in tissue-mimicking phantoms with known optical parameters and in mouse models of ischemic injury and heat stress. Experimental data obtained indicate variations in the CRI among brain tissue suffering from injury. CRI fluctuations correlated with alterations in the scattering and absorption coefficients of the injured tissue are demonstrated. This technique for deriving dynamic changes in the CRI of tissue may be further developed as a clinical diagnostic tool and for biomedical research applications. To the best of our knowledge, this is the first report of the estimation of the spectral CRI of a mouse head following injury obtained in the spatial frequency domain.
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