Sub-diffuse optical biomarkers characterize localized microstructure and function of cortex and malignant tumor

Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection

In this study, a general and systematical investigation of sub-diffuse reflectance spectroscopy is implemented. A Gegenbauer-kernel phase function-based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient μ a , reduced scattering coefficient μ s ' and sub-diffusive quantifier γ , simultaneously, at multiple source-detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase-lock-in technique. Furtherly, the trained neural-network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO₂), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO₂, lipid and water data were achieved particularly well within the wavelength ranges 350-590 nm and 600-1230 nm for the short-SDD probe, and 380-400 nm, 420-610 nm, and 650-950 nm for the long-SDD probe.

Methods of extraction of optical properties from diffuse reflectance measurements of ex-vivo human colon tissue using thin film silicon photodetector arrays

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a promising technique for characterization of colon tissue. Herein, two methods for extracting the reduced scattering and absorption coefficients ( μ s ' ( λ ) and μ a ( λ ) ) from SRDRS data using lookup tables of simulated diffuse reflectance are reported. Experimental measurements of liquid tissue phantoms performed with a custom multi-pixel silicon SRDRS sensor spanning the 450 - 750 nm wavelength range were used to evaluate the extraction methods, demonstrating that the combined use of spatial and spectral data reduces extraction error compared to use of spectral data alone. Additionally, SRDRS measurements of normal and tumor ex-vivo human colon tissue are presented along with μ s ' ( λ ) and μ a ( λ ) extracted from these measurements.

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study

Light propagation in biological tissues is frequently modeled by the Monte Carlo (MC) method, which requires processing of many photon packets to obtain adequate quality of the observed backscattered signal. The computation times further increase for detection schemes with small acceptance angles and hence small fraction of the collected backscattered photon packets. In this paper, we investigate the use of a virtually increased acceptance angle for efficient MC simulation of spatially resolved reflectance and estimation of optical properties by an inverse model. We devise a robust criterion for approximation of the maximum virtual acceptance angle and evaluate the proposed methodology for a wide range of tissue-like optical properties and various source configurations.

Determination of three optical properties from subdiffusive spatially resolved reflectance calculations

We report a theoretical study on the determination of three optical properties from spatially resolved reflectance calculations. In particular, the reduced scattering coefficient μs′, the absorption coefficient μa, and the recently defined phase function parameter σ are identified. The solution of the inverse problem is based on the principal component analysis of a large set of reflectance profiles that were calculated using an analytical solution of the radiative transfer equation. Different phase function types were studied to test the method in the range of 0.63  mm−1≤μs′≤4.2  mm−1 and 0.002  mm−1≤μa≤0.1  mm−1. For curves impaired with noise, we were able to reconstruct μs′ and μa with relative median errors of 2.5% and 12%, respectively, and σ with an absolute median error of 0.04.

Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance

Estimation of optical properties from subdiffusive reflectance acquired at short source-detector separations is challenging due to the sensitivity to the underlying scattering phase function. In recent studies, a second-order similarity parameter γ has been increasingly used alongside the absorption and reduced scattering coefficients to account for some of the phase function variability. By using Monte Carlo simulations, we show that the influence of the scattering phase function on the subdiffusive reflectance for the biologically relevant variations can be captured sufficiently well by considering γ and a third-order similarity parameter δ. Utilizing this knowledge, we construct an inverse model that estimates the absorption and reduced scattering coefficients, γ and δ, from spatially resolved reflectance. Nearly an order of magnitude smaller errors of the estimated optical properties are obtained in comparison to the inverse model that only composes γ.

Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media

Analytical expressions for sampling the scattering angle from a phase function in Monte Carlo simulations of light propagation are available only for a limited number of phase functions. Consequently, numerical sampling methods based on tabulated values are often required instead. By using Monte Carlo simulated reflectance, we compare two existing and propose an improved numerical sampling method and show that both the number of the tabulated values and the numerical sampling method significantly influence the accuracy of the simulated reflectance. The provided results and guidelines should serve as a good starting point for conducting computationally efficient Monte Carlo simulations with numerical phase function sampling.

Monochromatic subdiffusive spatial frequency domain imaging provides in-situ sensitivity to intratumoral morphological heterogeneity in a murine model

For the first time, spatially resolved quantitative metrics of light scattering recovered with sub-diffusive spatial frequency domain imaging (sd-SFDI) are shown to be sensitive to changes in intratumoral morphology and viability by direct comparison to histopathological analysis. Two freshly excised subcutaneous murine tumor cross-sections were measured with sd-SFDI, and recovered optical scatter parameter maps were co-registered to whole mount histology. Unique clustering of the optical scatter parameters μs' vs. γ (i.e. diffuse scattering vs. relative backscattering) evaluated at a single wavelength showed complete separation between regions of viable tumor, aggresive tumor with stromal growth, varying levels of necrotic tumor, and also peritumor muscle. The results suggest that with further technical development, sd-SFDI may represent a non-destructive screening tool for analysis of excised tissue or a non-invasive approach to investigate suspicious lesions without the need for exogenous labels or spectrally resolved imaging.