Light Propagation In Biological Tissue Research Articles

Development of NIR-II small animal living fluorescence imaging system

Fluorescence imaging technology can dynamically monitor gene and cell changing in live animals in real-time, with advantages such as high sensitivity, high resolution, and non-invasion. In recent years, it has been widely used in tumor research, gene expression research, drug development research, etc. The imaging wavelength of traditional fluorescence imaging technology falls in the visible and near-infrared-I region. Due to the absorption and scattering effects of light propagation in biological tissues, and the inherent fluorescence of biological tissues, traditional fluorescence imaging techniques still have significant limitations in penetration depth and image signal-to-noise ratio. In this work, a highly integrated near-infrared-II (NIR-II, 900—1880 nm) small animal living fluorescence imaging system is developed by taking the advantages of NIR-II fluorescence imaging technology, such as low absorption, low scattering, and deep penetration depth in biological tissues. And a method of enhancing and correcting fluorescence image is proposed to optimize fluorescence images. In this work, the biological tissue simulation experiments and live animal experiments are conducted to test the performance and imaging effect of the system. The experimental results show that the system has the advantages of deep penetration depth, high signal-to-noise ratio, and high sensitivity. Combined with commercial indocyanine green reagents and aggregation-induced emission dyes, this system can monitor the distribution of blood vessels in real time and continuously monitor deep tissues and organs in mice, and conduct the dynamically monitoring research in living mice in a conscious state. This helps to promote tumor research and drug development research in the field of biomedical imaging to enter a new stage.

A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification.

Modeling optical fluence and diffuse reflectance distribution in normal and cancerous breast tissues exposed to planar and Gaussian NIR beam shapes using Monte Carlo simulation

Precise knowledge about light propagation in biological tissues is necessary for accurate diagnostics and effective therapies utilizing optical technologies. In the current paper, the Monte Carlo simulation is applied to study light dispersion in normal and cancerous breast after irradiating to different laser beam shapes. Two distinct laser wavelengths (800-1100nm) with planar and Gaussian shapes were employed. The spatially resolved steady-state diffuse reflectance of normal tissue and tumor was investigated using Monte Carlo simulation method via MCML and MCXLAB computations. The diffusion equation was solved to simulate the fluence rate at the tissue surface based on the optical parameter values (i.e., scattering and absorption coefficients). The results confirm differences in diffuse reflectance and optical fluence distribution between the normal and tumor tissues at each wavelength. Tissue optical parameters and the utilized laser beam shape control the distribution of the fluence rate within tissues. Therefore, offering visual representations of these distributions can provide a secure visual route for biological diagnostics.

Estimation of laser light propagation in biological tissue during laser-tissue bonding using Monte Carlo simulation

Monte Carlo simulations are an elementary approach towards modeling light propagation in tissues. The detection of subsurface temperature in tissues during laser mediated therapies and microsurgeries is crucial for estimating associated thermal damage. MC simulation provides with the possibility to optimize the process. The approach has been used to model light propagation, associated energy deposition, and the temperature distribution inside the tissue. Understanding the extent of laser light transmittance and heat distribution within the tissue is crucial for minimizing damage to the adjacent biological tissues. The total photon weight absorbed estimates the total heat distribution within the volume. A three-layer heterogeneous tissue was specified consisting of only the epidermis, dermis, and the subcutaneous fat tissue. The hop, drop, and spin trail of photons depends on the optical properties of these layers. The energy deposition and temperature distribution estimation are obtained by the MC simulation method. A real-time measurement of the temperature profile was also performed. The experimental results were in close congruence with the simulation result. The simulation results show good reproducibility of the real temperature distribution. Monte Carlo method can, thereby, be used in estimation and optimization laser induced processes.

Tutorial on Monte Carlo simulation of photon transport in biological tissues [Invited

A tutorial introduction to Monte Carlo (MC) simulation of light propagation in biological tissues. MC statistical sampling is introduced, the basic design of a MC program is explained, and examples of application in biomedicine are presented.

Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation

Near infrared diffuse optical tomography (DOT) is a potential tool for diagnosing cancer by image reconstruction of tissue optical properties. A variety of image reconstruction methods for DOT have been attempted, in general, based on the diffusion equation (DE). However, the image quality is still insufficient to clinical use, which is mainly attributed to the fact that the DE is invalid in some regions, such as low-scattering regions, and the inverse problem is inherently ill-posed. In contrast, the radiative transfer equation (RTE) accurately describes light propagation in biological tissue and also the DOT by deep learning is recently thought to be an alternative approach to the inverse problem. Distribution of time of flight (DTOF) of photons estimated by the time-domain RTE lends itself to deep learning along a temporal sequence. In this study, we propose a new DOT image reconstruction algorithm based on a long-short-term memory and the time-domain RTE. In simulation studies, using this algorithm, we succeeded in detection of an absorbing inclusion with a diameter of 5 mm, an absorber mimicking cancer, which was embedded in a two-dimensional square model (4 cm × 4 cm) with an optically homogeneous background. Multiple absorbers and a bigger absorber embedded in this model were also detected. We also demonstrate that, if simulation data by beam injection from multiple directions are employed as a training set, the accuracy of detection is improved especially for multiple absorbers.

Comparison of fluorescence lifetime and multispectral imaging for quantitative multiplexing in biological tissue.

Fluorescence lifetime (FLT) multiplexing and multispectral imaging (MSI) are both frequently employed for in vitro and ex vivo biological studies. In vivo applications of MSI for deep seated fluorophores require consideration of diffusive light propagation in biological tissue. We have previously shown that a well-known redshift of fluorescence spectra in diffusive medium induces a fluorophore cross-talk, which cannot be accounted for even with known optical properties of the medium. In contrast, FLT measurements remain largely unaffected by light propagation in tissue, enabling zero cross-talk and accurate relative quantification. While a fully quantitative estimation of fluorophore concentrations requires depth resolved tomographic imaging, this is often not possible due to the difficulty of estimating tissue optical properties and modelling light propagation in complex tissue geometries. Here, we experimentally investigate the performance of planar (non-tomographic) MSI and FLT multiplexing for the quantitative recovery of multiple near-infrared fluorophores embedded in 4-8 mm thick tissue. We show that FLT multiplexing provides a superior quantification accuracy (error < 10%) compared to MSI (error = 20-107%) in tissue. The error rates for MSI increased with tissue thickness and can be directly attributed to the spectral redshift induced cross-talk between emission spectra. Our data indicate that planar FLT multiplexing can provide high quantification accuracy in thick biological tissue without a need for optical property estimation, thereby offering an important validation tool for rapid quantification of fluorophore concentrations in bulk tissue.

Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE.

.Significance: Open-source software packages have been extensively used in the past three decades in medical imaging and diagnostics, aiming to study the feasibility of the application ex vivo. Unfortunately, most of the existing open-source tools require some software engineering background to install the prerequisite libraries, choose a suitable computational platform, and combine several software tools to address different applications.Aim: To facilitate the use of open-source software in medical applications, enabling computational studies of treatment outcomes prior to the complex in-vivo setting.Approach: FullMonteWeb, an open-source, user-friendly web-based software with a graphical user interface for interstitial photodynamic therapy (iPDT) modeling, visualization, and optimization, is introduced. The software can perform Monte Carlo simulations of light propagation in biological tissues, along with iPDT plan optimization. FullMonteWeb installs and runs the required software and libraries on Amazon Web Services (AWS), allowing scalable computing without complex set up.Results: FullMonteWeb allows simulation of large and small problems on the most appropriate compute hardware, enabling cost improvements of versus always running on a single platform. Case studies in optical property estimation and diffuser placement optimization highlight FullMonteWeb’s versatility.Conclusion: The FullMonte open source suite enables easier and more cost-effective in-silico studies for iPDT.

Rationale for the Choice of the Ellipsoidal Reflector Parameters for Biomedical Photometers

Biomedical photometersʼ information-measuring systems with ellipsoidal reflectors have acceptable results in determining of biological tissues optical properties in the visible and near-infrared spectral range. These photometers make it possible to study the optical radiation propagation in turbid media for direct and inverse problems of light-scattering optics. The purpose of this work is to study the influence of the ellipsoidal reflectors design parameters on the results of biomedical photometry when simulating the optical radiation propagation in a system of biological tissue and reflectors in transmitted and reflected light.The paper substantiates the choice of the ellipsoidal reflectors’ focal parameter for efficient registration of forward and backscattered light. The methodology of the process is illustrated by the results of a model experiment using the Monte Carlo simulation for samples of human brain white and gray matter at the visible range of 405 nm, 532 nm, and 650 nm. The total transmittance, diffuse reflectance, and absorption graphs depending on the sample thickness were obtained. Based on the introduced concepts of the ellipsoidal reflector efficiency index and its efficiency factor, the expediency of choosing the ellipsoidal reflectors focal parameter is analyzed to ensure the registration of the maximum amount of scattered light. The graphs of efficiency index in reflected and transmitted light for different thickness samples of white and gray matter and efficiency factors depending on the sample thickness were obtained.The influence of the reflectors ellipticity on the illuminance of various zones of photometric images using the example of an absorbing biological medium – pig liver tissue – at wavelength of 405 nm with a Monte Carlo simulation was analyzed.The optical properties of biological media (scattering and absorption coefficients, scattering anisotropy factor, refractive index) and the samples’ geometric dimensions, particularly the thickness, are predetermined when choosing the ellipsoidal reflectors parameters for registration of the scattered light. Coordinates of the output of photons and their statistical weight obtained in the Monte Carlo simulation of light propagation in biological tissue have a physical effect on a characteristic scattering spot formation in the receiving plane of a biomedical photometer with ellipsoidal reflectors.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry.

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

Diagnosing different types of skin carcinoma based on their optical properties: A Monte-Carlo implementation

Skin cancer is a very common and serious type of cancers worldwide. Among many kinds of non-melanoma skin cancers, Basel Cell and Squamous Cell Carcinoma are highly treatable in case of early detection. Various Diagnosing techniques are employed to detect skin cancer, such as dermoscopy, OCT, biopsy and physical examination according to the medical case. However, the non-invasive optical methods are gaining validity due to their competitive advantages including safety and functionality. In addition, they are painless and high sensitive to the examined tissue metabolic changes. The propagation of light in any biological tissue is controlled be its optical absorption and scattering properties that highly depend on the wavelength of the utilized light. Monte-Carlo simulation is a forward numerical method used to describe light propagation in biological tissues depending on their optical parameters. In this work, Monte-Carlo simulation method was implemented to characterize the light propagation in normal dermis, Infiltrative Basal Cell Carcinoma, Nodular Basal Cell Carcinoma, and Squamous Cell Carcinomas in order to differentiate healthy from cancerous tissues. The obtained results provided information about the amount of light reflectance, transmittance, absorbed fraction and fluence rate distribution in the examined tissues showing different values at each condition over a wide range of wavelengths, which provide a simple, safe and functional tool for diagnosing these categories of skin carcinoma.

Monte Carlo Simulations of the photo-thermal cancer therapy of melanin

In this study, we performed Monte Carlo calculations to investigate photo-thermal effects on melanoma therapy. For this purpose, we simulated the light propagation in biological tissue. The propagation of laser light originates from a source point inside a single layer of light skin with a concentration of melanosomes $$(f_{\text{mel}} = 3\% )$$ . This single layer includes the epidermal with a thickness of 60 $$\upmu{\text{m}}.$$ Various important wavelengths are applied for photo-thermal therapy (PTT), such as Alexandrite laser, Ruby laser, and Nd:YAG, respectively. Accordingly, a single layer of skin is exposed to laser. It is demonstrated that the exposure to laser is a suitable option at a wavelength of $$\lambda =$$ 755 nm, which is useful to improve the therapeutic treatment compared to the lasers of wavelengths of $$\lambda =$$ 694 nm and $$\lambda =$$ 1064 nm, respectively. It is therefore possible to enhance the thermal treatment for light-skinned patients. The evaluation of temporal and spatial distributions is investigated versus the temperature, while the effects of relaxation time and thermal damage on the epidermal are involved.

Anisotropic scattering characteristics of nanoparticles in different morphologies: improving the temperature uniformity of tumors during thermal therapy using forward scattering.

Precise control of the thermal damage area is the key issue during thermal therapy, which can be achieved by manipulating the light propagation in biological tissue. In the present work, a method is proposed to increase the uniformity of the specific absorption rate (SAR) distribution in tumors during laser-induced thermal therapy, which is proved to be effective in reducing the thermal damage of healthy tissue. In addition, a better way of manipulating light propagation in biological tissue is explored. It is found that the anisotropic scattering characteristics of nanoparticles are strongly dependent on their shapes, sizes, orientations, and incident wavelengths, which will strongly affect the light propagation in nanoparticle embedded biological tissue. Therefore, to obtain a better outcome from photothermal therapy, the scattering properties of nanoparticles are very important factors that need to be taken into consideration, along with the absorption efficiency. Further investigation finds that nanoparticles that predominantly scatter to the forward direction are favorable in obtaining a larger penetration depth of light, which will improve the uniformity of SAR and temperature distributions. This paper is meaningful for the application of nanoparticle-assisted laser-induced thermal therapy.

Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method

Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.

DETERMINATION OF THE TISSUE ANISOTROPY FACTOR DURING THE PHOTOMETRY BY ELLIPSOIDAL REFLECTORS

Background. The features of scattering anisotropy factor determination in the research of thick biological tissue samples.Objective. The purpose of the paper is development and testing of new method of tissue scattering anisotropy factor determination. The method is based on the analysis of illuminance of different zones of photometric images, received during the photometry by ellipsoidal reflectors.Methods. For implementation of ideas and assumptions, the paper applies basics of photometry by ellipsoidal reflectors for real and model experiments, with the implementation of direct and inverse Monte Carlo method for light propagation in biological tissue. Additionally, principles of zone analysis of photometric images illuminance, received during the photometry by ellipsoidal reflectors, are applied.Results. Based on the results of real experiment, the research represents input data set for Monte Carlo simulation of light propagation in biomedical photometer with ellipsoidal reflectors. Considering the modeling, the selection of critical thickness of samples of chicken and porcine muscle tissues was reasoned for further comparison with the results of real experiment. Dependencies of illuminance of different zones of photometric images for the selected thicknesses in the significant range of anisotropy factor value change were received. Anisotropy factors were determined in the spatial scattering cross sections. There was performed the comparative evaluation of character of photometric image zone illumination dependency for samples of various muscle tissues of similar thickness, and different thicknesses of selected tissue. Based on the developed method the specificities of real experiment results reproduction during the determination of scattering anisotropy factor by the illuminance values of photometric images, received during the simulation are shown.Conclusions. Method of the mirror ellipsoid of revolution (which was used during the experiment with biological tissues samples in reflected and transmitted light) isn’t limited by the functionality of preliminary estimation of quantity of cross section for further spatial analysis and investigation of scattering indicatrix. Photometry by ellipsoidal reflectors can be applied as the separate method for determination of the magnitude of the scattering anisotropy factor based on the results of model and numerical experiments, and the developed procedure.

Three‐dimensional frequency‐domain optical anisotropy imaging of biological tissues with near‐infrared light

Near-infrared optical imaging aims to reconstruct the absorption and scattering coefficients in order to detect tumors at early stage. However, the reconstructions have only been limited to and due to theoretical and computational limitations. The authors propose an efficient method of the reconstruction, in three-dimensional geometries, of the anisotropy factor g of the Henyey-Greenstein phase function as a new optical imaging biomarker. The light propagation in biological tissues is accurately modeled by the radiative transfer equation (RTE) in the frequency-domain. The reconstruction algorithm is based on a gradient-based updating scheme. The adjoint method is used to efficiently compute the gradient of the objective function which represents the discrepancy between simulated and measured boundary data. A parallel implementation is carried out to reduce the computational time. We show that by illuminating only one surface of a tissue-like phantom, the algorithm is able to accurately reconstruct optical values and different shapes (spherical and cylindrical) that characterize small tumor-like inclusions. Numerical simulations show the robustness of the algorithm to reconstruct the anisotropy factor with different contrast levels, inclusion depths, initial guesses, heterogeneous background, noise levels, and two-layered medium. The crosstalk problem when reconstructing simultaneously and g has been reported and achieved with a reasonable quality. The proposed RTE-based reconstruction algorithm is robust to spatially retrieve and localize small tumoral inclusions. Heterogeneities in g-factor have been accurately reconstructed which makes the new algorithm a candidate of choice to image this factor as new intrinsic contrast biomarker for optical imaging.

In situ estimation of optical properties of rat and monkey brains using femtosecond time-resolved measurements

An accurate knowledge of tissue optical properties (absorption coefficients, μa, and reduced scattering coefficients, μs’) is critical for precise modeling of light propagation in biological tissue, essential for developing diagnostic and therapeutic optical techniques that utilize diffusive photons. A great number of studies have explored the optical properties of various tissue, and these values are not known in detail due to difficulties in the experimental determination and significant variations in tissue constitution. Especially, in situ estimates of the optical properties of brain tissue, a common measurement target in optical imaging, is a challenge because of its layer structure (where the thin gray matter covers the white matter). Here, we report an approach to in situ estimates of the μa and μs’ of the gray and white matter in living rat and monkey brains by using femtosecond time-resolved measurements and Monte Carlo simulation. The results demonstrate that the μa of the gray matter is larger than that of the white matter, while there was no significant difference in the μs’ between the gray and white matter. The optical properties of the rat brain were very similar to those of the monkey brain except for the μa of the gray matter here.

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.

.Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Light propagation in biological tissue

We developed a new parallel computational method for solving fluorescence and elastically scattered light propagation through a biological tissue illuminated by a collimated incident beam. The three-dimensional steady state radiative transfer equation was solved using a Modified Finite Volume Method with a cell-vertex formulation. An Exponential spatial differencing scheme was used to efficiently lessen the false scattering. Several test problems are presented to assess the performance and accuracy of the numerical Method. We show that it is possible to obtain a very good level of accuracy. Relative differences less than 1.5% were obtained in comparison with the Monte Carlo reference solution for the selected problems. This study shows the potential of the proposed computational method to be used as an accurate deterministic forward solver in Optical Tomography.

Real time evaluation of tissue optical properties during thermal ablation of ex vivo liver tissues

Complete ablation of liver tumors is vital for minimizing the risk of local tumor recurrence. Accurately identifying the hallmarks of tissue necrosis during thermal ablative therapies may significantly increase the efficacy of ablation, while minimizing unnecessary damage to the surrounding normal tissues or critical structures. Light propagation in biological tissues is sensitive to the tissue microstructure and chromophore concentrations. In our previous studies, we found that the wavelength (λ) averaged liver tissue absorption coefficient (µa) and reduced scattering coefficient (µs′) change significantly upon heating which may be used for assessment of tissue damage during thermal ablation of solid tumors. Here, we seek to demonstrate the use of an integrated fiber-optic probe for continuous monitoring of the local tissue temperature (T), µa(λ) and µs′(λ) during thermal ablation of ex vivo porcine livers. The wavelength-averaged (435–630 nm) tissue absorption and scattering (µa and µs′ ) increased rapidly at 45 °C and plateaued at 67 °C. The mean µa and µs′ for liver tissue at 37 °C (n = 10) were 8.5 ± 3.7 and 2.8 ± 1.1 cm−1, respectively. The relative changes in µa and µs′ at 37, 55, and 65 °C were significantly different (p < .02) from each other. A relationship between the relative changes in µa and µs′ and the degree of tissue damage estimated using the temperature-based Arrhenius model for porcine liver tissues was established and studied.