Near-infrared Light Propagation Research Articles

Unidirectional reflectionless propagation of near-infrared light in heterogeneous metamaterials

Unidirectional reflectionless propagation of near-infrared light in heterogeneous metamaterials

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model.

Functional near-infrared spectroscopy (fNIRS) is an imaging method in which a light source and detector are installed on the head; consequently, the re-emission of light from human skin contains information about cerebral hemodynamic alteration. The spatial probability distribution profile of photons penetrating tissue at a source spot, scattering into the tissue, and being released at an appropriate detector position, represents the spatial sensitivity. Modeling light propagation in a human head is essential for quantitative near-infrared spectroscopy and optical imaging. The specific form of the distribution of light is obtained using the theory of perturbation. An analytical solution of the perturbative diffusion equation (DE) and finite element method (FEM) in a Slab media (similar to the human head) makes it possible to study light propagation due to absorption and scattering of brain tissue. The simulation result indicates that sensitivity is slowly decreasing in the deep area, and the sensitivity below the source and detector is the highest. The depth sensitivity and computation time of both analytical and FEM methods are compared. The simulation time of the analytical approach is four times larger than the FEM. In this paper, an analytical solution and the performance of FEM methods when applied to the diffusion equation for heterogeneous media with a single spherical defect are compared. The depth sensitivity along with the computation time of simulation has been investigated for both methods. For simple and Slab modes of the human brain, the analytical solution is the right candidate. Whenever the brain model is sophisticated, it is possible to use FEM methods, but it costs a higher computation time. Analytical and finite element method (FEM) depth sensitivity are almost the same.FEM requires more computation time, but can handle complicated head models.The analytical method is proposed for the first step and simple head models. The functional near-infrared spectroscopy (fNIRS) is a type of neuromonitoring that uses near-infrared light to measure brain activity indirectly and is similar to electroencephalography (EEG). A single-channel fNIRS system contains a near-infrared light source, which emits near-infrared light (NIR), and a detector is placed near the source. A light intensity change received by detectors indicates brain activity when NIR light penetrates into the gray matter. It is necessary to have a prior understanding of light penetration depth in order to measure brain activity more accurately. fNIRS can be better understood, optimized, and investigated through modeling light propagation in brain tissue. In order to study light in tissues, analytical and numerical methods can be used. In this work, we compared these two approaches quantitatively in a simple slab medium. We concluded that the numerical method takes too much time to calculate the results, but it can be applied to complicated head models. The results of these studies provide researchers with new insights into the modeling and simulation of fNIRS and diffuse optical tomography.

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

Numerical Study of Near-Infrared Light Propagation in Aqueous Alumina Suspensions Using the Steady-State Radiative Transfer Equation and Dependent Scattering Theory

Understanding light propagation in liquid phantoms, such as colloidal suspensions, involves fundamental research of near-infrared optical imaging and spectroscopy for biological tissues. Our objective is to numerically investigate light propagation in the alumina colloidal suspensions with the mean alumina particle diameter of 55 nm at the volume fraction range 1–20%. We calculated the light scattering properties using the dependent scattering theory (DST) on a length scale comparable to the optical wavelength. We calculated the steady-state radiative transfer and photon diffusion equations (RTE and PDE) using the DST results based on the finite difference method in a length scale of the mean free path. The DST calculations showed that the scattering and reduced scattering coefficients become more prominent at a higher volume fraction. The anisotropy factor is almost zero at all the volume fractions, meaning the scattering is isotropic. The comparative study of the RTE with the PDE showed that the diffusion approximation holds at the internal region with all the volume fractions and the boundary region with the volume fraction higher than 1%. Our findings suggest the usefulness of the PDE as a light propagation model for the alumina suspensions rather than the RTE, which provides accurate but complicated computation.

Unidirectional Reflectionless Propagation of Near-Infrared Light in Heterogeneous Metamaterials

Unidirectional Reflectionless Propagation of Near-Infrared Light in Heterogeneous Metamaterials

Unidirectional reflectionless propagation of near-infrared light in resonator-assisted non-parity-time symmetric waveguides

The unidirectional reflectionless (UR) light propagation is investigated in the waveguide coupled to gain and loss resonators by using a developed coupled mode-scattering matrix theory. The results show that there is almost no reflection in the case of the backward incidence, but total reflection in the case of the forward incidence under the condition of balancing gain and loss in the gain resonator for the proposed waveguide when the indirect coupling phase θ ranges from 0.8 rad to 2.3 rad and from 4 rad to 5.5 rad. Moreover, the coherent perfect absorption (CPA) can be observed at the same time. Especially, the UR light propagation appears when the absolute value of detuning δ is smaller than 1 × 1013 rad s−1. Based on the findings above, we propose a metal–insulator–metal non-parity-time symmetric plasmonic waveguide and obtain the UR plasmonic propagation and CPA. The theoretical results are in excellent agreement with the finite-difference time-domain simulations. These results will provide a new pathway for the realization of unidirectional propagation and absorption of light at the nanoscale.

Prohibition of infrared electromagnetic waves through optical coatings on plain glasses

Abstract Ultraviolet and infrared radiations originated from the sunlight have severe effects on the human body such as sunburn and skin tanning are only a few of them. Therefore, there is a necessity of optical coatings on the window glasses used in the houses, schools, hospitals, offices etc. for preventing the exposure of harmful radiations. In this work, we present the sol–gel spin coating of TiO2 and SiO2 materials as the optical dielectric reflector for prohibiting the propagation of near-infrared light. The prepared 2.5 based TiO2/SiO2 reflector was characterized to examine the surface topography, cross-sectional morphology, and optical reflectance. Furthermore, the prepared sample was tested for its thermal behaviour using laboratory-developed set-up. The reflector based on the 2.5 bilayers showed about 78.4 °C temperature difference between the ‘in’ and ‘out’ temperatures after radiating the sample with an infrared light source. The obtained thermal response result indicates the promising application of such coatings in prohibiting the exposure of harmful radiation in the working/residential buildings.

Probe Design Optimization for Time-Domain NIROT "Pioneer" System for Imaging the Oxygenation of the Preterm Brain.

In preterm infants, there is a risk of life-lasting impairments due to hemorrhagic/ischemic lesions. Our time-domain (TD) near-infrared optical tomography (NIROT) system "Pioneer" aims at detecting both disorders with high spatial resolution. Successfully tested on phantoms, "Pioneer" entered the phase of improvements and enhancements. The current probe (A-probe) was adapted for an optoacoustics instrument. A new probe (B-probe) optimized for TD measurements is required. Our aim is to determine the optimal arrangement of light sources in the B-probe to increase the sensitivity and the resolution of Pioneer and to improve the ability of the system to detect both ischemia and hemorrhage. To do this, we simulated TD-NIROT signals in NIRFAST, a MATLAB-based package used to model near-infrared light propagation through tissue. We used 16×16 detector array, with ~2.2 mm distance between the detectors. Light sources were arranged around the field of view (FoV). We performed forward simulations of light propagation through a "homogeneous case" (HC) tissue (μ's=5.6 cm-1, μa=0.07 cm-1). Next, we simulated light propagation through "inhomogeneous case" -tissue' (IC) tissue by adding ischemia (μa=μa·2.5 cm-1) or hemorrhage (μa=μa·50 cm-1) to HT as a spherical inclusion of 5 mm radius at different depths in the FoV center and identified the source location that provides the higher contrast on the FoV: maxi∈I (FoVContrastSOURCE). It was found that sources located closer to the FoV center generate greater contrast for late photons. This study suggests the light sources in B-probe should be closer to the FoV center. The higher sensitivity is expected to lead to a higher image quality.

近红外光在皮肤组织中的传播和光纤探测结构设计

近红外光在皮肤组织中的传播和光纤探测结构设计

Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation

Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation

Optical tomographic imaging for breast cancer detection.

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

A Tensor B-Spline Approach for Solving the Diffusion PDE With Application to Optical Diffusion Tomography

Optical Diffusion Tomography (ODT) is a modern non-invasive medical imaging modality which requires mathematical modelling of near-infrared light propagation in tissue. Solving the ODT forward problem equation accurately and efficiently is crucial. Typically, the forward problem is represented by a Diffusion PDE and is solved using the Finite Element Method (FEM) on a mesh, which is often unstructured. Tensor B-spline signal processing has the attractive features of excellent interpolation and approximation properties, multiscale properties, fast algorithms and does not require meshing. This paper introduces Tensor B-spline methodology with arbitrary spline degree tailored to solve the ODT forward problem in an accurate and efficient manner. We show that our Tensor B-spline formulation induces efficient and highly parallelizable computational algorithms. Exploitation of B-spline properties for integration over irregular domains proved valuable. The Tensor B-spline solver was tested on standard problems and on synthetic medical data and compared to FEM, including state-of-the art ODT forward solvers. Results show that 1) a significantly higher accuracy can be achieved with the same number of nodes, 2) fewer nodes are required to achieve a prespecified accuracy, 3) the algorithm converges in significantly fewer iterations to a given error. These findings support the value of Tensor B-spline methodology for high-performance ODT implementations. This may translate into advances in ODT imaging for biomedical research and clinical application.

Study of a simple model for the transition between the ballistic and the diffusive regimes in diffusive media.

A Monte Carlo simulation was utilized to investigate a simple model for the transition between the ballistic and the diffusive regimes in diffusive media. The simulation focuses on the propagation of visible and near-infrared light in biological tissues. This research has mainly two findings: (1) the transition can be described, as was found experimentally, with good accuracy by only two terms (ballistic and diffusive). (2) The model can be utilized for cases where the absorption coefficient is not negligible compared to the scattering coefficient by adding a power-law prefactor to the diffusive term.

Generalized Beer-Lambert model for near-infrared light propagation in thick biological tissues.

The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modified the Beer–Lambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concentrations for NIR spectroscopic data analysis. Even though MBLL is effective in terms of providing qualitative comparison, it suffers from its applicability across tissue types and tissue dimensions. In this work, we introduce Lambert-W function-based modeling for light propagation in biological tissues, which is a generalized version of the Beer–Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coefficients μ0 and η. We validated our model against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. We included numerous human and animal tissues to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is first of its kind in providing accurate modeling of NIR light propagation in biological tissues.

Monte Carlo modeling of light propagation in the human head for applications in sinus imaging.

Sinus blockages are a common reason for physician visits, affecting one out of seven people in the United States, and often require medical treatment. Diagnosis in the primary care setting is challenging because symptom criteria (via detailed clinical history) plus objective imaging [computed tomography (CT) or endoscopy] are recommended. Unfortunately, neither option is routinely available in primary care. We previously demonstrated that low-cost near-infrared (NIR) transillumination correlates with the bulk findings of sinus opacity measured by CT. We have upgraded the technology, but questions of source optimization, anatomical influence, and detection limits remain. In order to begin addressing these questions, we have modeled NIR light propagation inside a three-dimensional adult human head constructed via CT images using a mesh-based Monte Carlo algorithm (MMCLAB). In this application, the sinus itself, which when healthy is a void region (e.g., nonscattering), is the region of interest. We characterize the changes in detected intensity due to clear (i.e., healthy) versus blocked sinuses and the effect of illumination patterns. We ran simulations for two clinical cases and compared simulations with measurements. The simulations presented herein serve as a proof of concept that this approach could be used to understand contrast mechanisms and limitations of NIR sinus imaging.

Effects of wavelength, beam type and size on cerebral low-level laser therapy by a Monte Carlo study on visible Chinese human

Low-level laser therapy (LLLT) has been clinically utilized for many indications in medicine requiring protection from cell/tissue death, stimulation of healing and repair of injuries, pain reduction, swelling and inflammation. Presently, the use of LLLT to treat stroke, traumatic brain injury and cognitive dysfunction are attracting growing interest. Near-infrared light is capable of penetrating into the cerebral cortex, allowing noninvasive treatments to be carried out with few treatment-related adverse events. Optimization of LLLT treatment effect is a crucial issue of this field; however, only a few experimental tests on mice for wavelength selection have been reported. We addressed this issue by low-cost, straightforward and quantitative comparisons on light dosage distribution within visible Chinese human head by Monte Carlo modeling of near-infrared light propagation. Optimized selection in wavelength, beam type and size were given based on comparisons among frequently used setups (i.e., wavelengths: 660, 810 and 980 nm; beam type: Gaussian and flat beam; beam diameter: 2, 4 and 6 cm). This study provided an efficient way for guiding the optimization of LLLT setup and selection on wavelength, beam type and size for clinical brain LLLT.

The Toast++ software suite for forward and inverse modeling in optical tomography.

We present the Toast++ open-source software environment for solving the forward and inverse problems in diffuse optical tomography (DOT). The software suite consists of a set of libraries to simulate near-infrared light propagation in highly scattering media with complex boundaries and heterogeneous internal parameter distribution, based on a finite-element solver. Steady-state, time- and frequency-domain data acquisition systems can be modeled. The forward solver is implemented in C++ and supports performance acceleration with parallelization for shared and distributed memory architectures, as well as graphics processing computation. Building on the numerical forward solver, Toast++ contains model-based iterative inverse solvers for reconstructing the volume distribution of absorption and scattering parameters from boundary measurements of light transmission. A range of regularization methods are provided, including the possibility of incorporating prior knowledge of internal structure. The user can link to the Toast++ libraries either directly to compile application programs for DOT, or make use of the included MATLAB and PYTHON bindings to generate script-based solutions. This approach allows rapid prototyping and provides a rich toolset in both environments for debugging, testing, and visualization.

Monte Carlo simulation of near-infrared light propagation through homogeneous mixed media

ABSTRACT. Noninvasive blood analysis devices that can measure levels of small constituents of blood are of interest in the medical community. An important step in creating these devices is to understand the interaction of photons with human tissue in increasingly greater physiological detail. Models based on layered biological materials give excellent results for many applications but may not be as accurate as needed when those materials are finely intertwined to the point of resembling a homogeneous mixture. To explore the ramifications of treating materials as layers versus a mixture, we have modeled, using a Monte Carlo technique, the interaction of photons through epidermis, blood, and water arranged both in layers and in a homogeneous blend. We confirm the expected linear relation between photon attenuation and material volumetric percentage in two-layer models. However, when the materials are homogeneously mixed together and volumetric percentage is replaced with interaction volume percentage, this relationship becomes nonlinear. These nonlinearities become significant when the values of the interaction coefficient, μt, differ by an order of magnitude or more.

Applying a new computational method for biological tissue optics based on the time-dependent two-dimensional radiative transfer equation

Optical tomography is a medical imaging technique based on light propagation in the near infrared (NIR) part of the spectrum. We present a new way of predicting the short-pulsed NIR light propagation using a time-dependent two-dimensional-global radiative transfer equation in an absorbing and strongly anisotropically scattering medium. A cell-vertex finite-volume method is proposed for the discretization of the spatial domain. The closure relation based on the exponential scheme and linear interpolations was applied for the first time in the context of time-dependent radiative heat transfer problems. Details are given about the application of the original method on unstructured triangular meshes. The angular space (4πSr) is uniformly subdivided into discrete directions and a finite-differences discretization of the time domain is used. Numerical simulations for media with physical properties analogous to healthy and metastatic human liver subjected to a collimated short-pulsed NIR light are presented and discussed. As expected, discrepancies between the two kinds of tissues were found. In particular, the level of light flux was found to be weaker (inside the medium and at boundaries) in the healthy medium than in the metastatic one.

Development of Red Blood Cell–Photon Simulator for Optical Propagation Analysis in Blood using Monte Carlo Method

We have developed a "red blood cell (RBC)-photon simulator" to reveal optical propagation in prethrombus blood for various levels of RBC density and aggregation. The simulator investigates optical propagation in the prethrombus blood and will be applied to detect it noninvasively for thrombosis prevention in an earlier stage. In our simulator, Lambert-Beer's law is employed to simulate the absorption of RBCs with hemoglobin, while the Monte Carlo method is applied to simulate scattering through iterative calculations. One advantage of our simulator is that concentrations and distributions of RBCs can be arbitrarily chosen to exhibit the prethrombus, while conventional models cannot. Using the simulator, we found that various levels of RBC density and aggregation have different effects on the optical propagation of near-infrared response light in blood. The same different effects were acquired in in vitro experiments with 12 bovine blood samples, which were performed to evaluate the simulator. We measured RBC density using the clinical hematocrit index and RBC aggregation using activated whole blood clotting time. The experimental results correspond to the simulator results well. Therefore, we could show that our simulator exhibits the correct optical propagation for prethrombus blood and is applicable for the prethrombus detection using multiple detectors.