Light Propagation Model Research Articles

Model on picometer-level light gravitational delay in the GRACE Follow-On-Like missions

Abstract Laser interferometry plays a crucial role for laser ranging in high-precision space missions such as GRACE (Gravity Recovery and Climate Experimen) Follow-On-Like missions and gravitational wave detectors. For such accuracy of modern space missions, a precise relativistic model of light propagation is requires. With the post-Newtonian approximation, we utilize the Synge world function method to study the light propagation in the Earth’s gravitational field, deriving the gravitational delays up to order c -4. Then, we investigate the influences of gravitational delays in the three inter-satellite laser ranging techniques, including one-way ranging, dual one-way ranging, and transponder-based ranging. By combining the parameters of Kepler orbit, the gravitational delays are expanded up to the order of e 2 (e is the orbital eccentricity). Finally, considering the GRACE Follow-On-Like missions, we estimate the gravitational delays to the level of picometer. The results demonstrate some high-order gravitational and coupling effects, such as c -4-order gravitational delays and coupling of Shapiro and beat frequency, may be non-negligible for higher precision laser ranging in the future.

Optimizing Underwater Image Restoration and Depth Estimation with Light Field Images

Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods.

Using spectral derivatives to remove the influence of hair on optical images of the static absorbing properties of tissue-like turbid media.

Although measurements of near-infrared light diffusely reflected from the head and other biological tissues are commonly used to generate images revealing changes in the concentrations of oxy- and deoxy-hemoglobin, static imaging of absolute concentrations has been inhibited by the unknown and variable coupling between the optical probe and the skin, to which hair is often a significant contributor. Measurements of spectral derivatives provide a means of overcoming this shortcoming. The aim is to demonstrate experimentally that measurements of the derivative of the attenuation of the detected signal with respect to wavelength can be used to achieve images that are immune to the spatial variation of hair on the surface. The objective is to generate topographic images representative of static absorbing properties rather than retrieving absolute optical coefficients, which requires a tomographic approach. The surface of a tissue-equivalent phantom, containing targets with different concentrations of absorbing dye, was coated with a layer of dark hair. The phantom was then imaged using a broadband source and spectrometer, and prior knowledge of the absorbing characteristics of the dye and of melanin was used to acquire separate images of each. The targets within the phantom are revealed with remarkable clarity, although a nonlinear relationship between the target contrast and absorption was observed. This nonlinear behavior was confirmed and explained using a Monte Carlo model of light propagation in a slab of similar absorbing properties. A spectral derivative approach could be an effective tool for in vivo topographic imaging of the static optical properties of the brain and other tissues, avoiding the deleterious effects of hair.

Analysis on the ultraviolet curing process of resin‐based glass fiber composites based on finite element mesoscale model

AbstractThe resin curing process triggered by ultraviolet radiation possesses numerous advantages, including rapid speed, low cost, and minimal environmental impact. Due to the hindrance of light propagation by the medium, uneven curing or incomplete curing at the bottom may occur frequently, particularly when the material thickness is excessive. To address these issues and delve into the regularities of the light‐curing process, this study employed a modeling approach that separately represents fiber bundles and the resin matrix to construct a mesoscopic model of the resin‐based glass fiber‐reinforced composite material glass fiber reinforced polymer (GFRP). Based on the models of light propagation, light‐curing kinetics, and heat conduction, a multiphysics field decoupling model is established to simulate the light‐curing process of GFRP. Using this model, the temporal and spatial variations of the internal light field, degree of curing field, and temperature field within GFRP during the curing process are simulated. The mechanistic reasons for these variations are analyzed. Finally, in conjunction with the regular analysis based on the macroscopic model, the accuracy of the model and the obtained regularities are validated through experiments. This provides an effective analytical method for the study of light‐curing in resin‐based fiber‐reinforced composite materials.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent on a prior information, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems. Approach. This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence on a priori information, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable region and the results are used as the basis for its next movement. Main Results. The performance of the proposed framework is evaluated with a series of simulations and in vivo experiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility. Significance. The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons.

Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities. In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter. Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i)a typical homogeneous reconstruction and (ii)a theoretical layered reconstruction. Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude). We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

Improving heart rate monitoring in the obese with time-of-flight photoplethysmography (TOF-PPG): a quantitative analysis of source-detector-distance effect.

Commercial photoplethysmography (PPG) sensors rely on the measurement of continuous-wave diffuse reflection signals (CW-DRS) to monitor heart rate. Using Monte Carlo modeling of light propagation in skin, we quantitatively evaluate the dependence of continuous-wave photoplethysmography (CW-PPG) in commercial wearables on source-detector distance (SDD). Specifically, when SDD increases from 0.5 mm to 3.3 mm, CW-PPG signal increases by roughly 846% for non-obese (NOB) skin and roughly 683% for morbidly obese (MOB) skin. Ultimately, we introduce the concept of time-of-flight PPG (TOF-PPG) which can significantly improve heart rate signals. Our model shows that the optimized TOF-PPG improves heart rate monitoring experiences by roughly 47.9% in NOB and 93.2% in MOB when SDD = 3.3 mm is at green light. Moving forward, these results will provide a valuable source for hypothesis generation in the scientific community to improve heart rate monitoring.

A precise compensation algorithm for visual measurement in non-uniform refractive index environment based on Hamiltonian ray tracing method

In complex large-scale industrial environments, light rays no longer propagate in a straight line due to the influence of non-uniform refractive index, which will lead to large measurement errors in visual measurement. In order to accurately compensate the error, this paper firstly quantitatively analyzes the influence of non-uniform refractive index distribution on visual measurement. Then the symplectic matrix ray-tracing method of Hamiltonian optics is used to reconstruct spatial light in the reverse direction with known light propagation end point and spatial refractive index distribution. Thus the traditional model of light propagation along a straight line in visual measurement can be changed and the measurement error introduced by non-uniform refractive index field can be corrected. Finally, the proposed Hamiltonian ray tracing method was verified through the spatial non-uniform refractive index field reconstructed by Background Oriented Schlieren(BOS) method. The experimental results verified the effectiveness and feasibility of this method.

Artificial intelligence and Fourier optics: Application of DeepLabV3+ in the recovery of a diffracting aperture in light propagation

The combination of Fourier Optics and Artificial Intelligence has driven significant advances in image processing and modeling of optical systems, with the UNet architecture being the main protagonist. However, the DeepLabV3+ network has recently shown promising performance in detecting transfer opens. In this study, we investigate the effectiveness of DeepLabV3+ in identifying transfer apertures in light propagation models and compare its performance with that of UNet. The results reveal that DeepLabV3+ outperforms UNet in terms of accuracy and robustness in identifying transfer apertures, even in the presence of noise and aperture shape variations.

Modeling of light transmission in multilayer epitaxial AlN/GaN structures for biomedical pyrosensors

The spectral-pulse diversity of modern lasers currently used for subcutaneous surgeries provides ample opportunities for the realization of rather complex modes of radiation exposure and allows the simultaneous performance of diagnostic, therapeutic and surgical procedures; this in turn leads to an urgent need for appropriate spectrally indifferent sensors oriented for in vivo operation. In the present work, we propose the use of weakly absorbing epitaxial GaN/AlN/GaN structures as a basis for the fabrication of pass-through pyrometric sensors embedded in a subcutaneous light guide. The use of such sensors in vivo will allow the local study of the absorption or scattering of intracavitary tissues in a wide spectral range, as well as the real-time control of the pulse structure of the laser beam. For such structures, a mathematical model of light propagation taking into account absorption and heat generation was constructed and a computational algorithm for obtaining post-pulse temperature distributions and for calculating the pyrocoefficient at different values of thickness and donor concentration of absorbing GaN layers was developed in the MATLAB environment. A criterion for the efficiency of the sensor performance was proposed, based on which the optimal ratios between the thickness values of AlN and GaN layers for different values of the absorption coefficient αGaN were obtained. Key words: subcutaneous operations, laser therapy, laser surgery, pyroeffect, pyrosensors, aluminum nitride, epitaxial technologies, multilayer GaN/AlN structures.

Quantitative photoacoustic tomography: modeling and inverse problems.

Quantitative photoacoustic tomography (QPAT) exploits the photoacoustic effect with the aim of estimating images of clinically relevant quantities related to the tissue's optical absorption. The technique has two aspects: an acoustic part, where the initial acoustic pressure distribution is estimated from measured photoacoustic time-series, and an optical part, where the distributions of the optical parameters are estimated from the initial pressure. Our study is focused on the optical part. In particular, computational modeling of light propagation (forward problem) and numerical solution methodologies of the image reconstruction (inverse problem) are discussed. The commonly used mathematical models of how light and sound propagate in biological tissue are reviewed. A short overview of how the acoustic inverse problem is usually treated is given. The optical inverse problem and methods for its solution are reviewed. In addition, some limitations of real-life measurements and their effect on the inverse problems are discussed. An overview of QPAT with a focus on the optical part was given. Computational modeling and inverse problems of QPAT were addressed, and some key challenges were discussed. Furthermore, the developments for tackling these problems were reviewed. Although modeling of light transport is well-understood and there is a well-developed framework of inverse mathematics for approaching the inverse problem of QPAT, there are still challenges in taking these methodologies to practice. Modeling and inverse problems of QPAT together were discussed. The scope was limited to the optical part, and the acoustic aspects were discussed only to the extent that they relate to the optical aspect.

Optimization of diffuse Raman spectroscopy for in-vivo quantification of foreign body response in a small animal model.

Diffuse Raman spectroscopy (DRS) allows subsurface molecular analysis of optically turbid samples. Numerical modeling of light propagation was used as a method for improving the design of an DRS instrument to maximize the signal to noise ratio (SNR) while ensuring safe laser exposure parameters required for in-vivo measurements. Experimental validation of the model was performed on both phantom samples and disks implanted postmortem to mimic the typical response to foreign bodies (formation of a fibrotic capsule around an implant). A reduction of laser exposure of over 1500-fold was achieved over previous studies whilst maintaining the same Raman collection rates and reaching the safe power density of 3 mW/mm2. The validation of this approach in a subcutaneous implant in a mouse cadaver showed a further improvement of 1.5-fold SNR, with a thickness limit of detection for the fibrotic layer of 23 µm, under the same acquisition times. In the animal body, a thickness limit of detection of 16 µm was achieved. These results demonstrate the feasibility of numerical model-based optimization for DRS, and that the technique can be improved sufficiently to be used for in-vivo measurement of collagenous capsule formation as a result of the foreign body response in murine models.

Laparoscopic Photoacoustic Imaging System Based on Side-Illumination Diffusing Fibers.

To develop a flexible miniaturized photoacoustic (PA) imaging probe for detecting anatomical structures during laparoscopic surgery. The proposed probe aimed to facilitate intraoperative detection of blood vessels and nerve bundles embedded in tissue not directly visible to the operating physician to preserve these delicate and vital structures. We modified a commercially available ultrasound laparoscopic probe by incorporating custom-fabricated side-illumination diffusing fibers that illuminate the probe's field of view. The probe geometry, including the position and orientation of the fibers and the emission angle, was determined using computational models of light propagation in the simulation and subsequently validated through experimental studies. In wire phantom studies within an optical scattering medium, the probe achieved an imaging resolution of 0.43 ±0.09 mm and a signal-to-noise ratio of 31.2±1.84 dB. We also conducted an ex vivo study using a rat model, demonstrating the successful detection of blood vessels and nerves. Our results indicate the viability of a side-illumination diffusing fiber PA imaging system for guidance during laparoscopic surgery. The potential clinical translation of this technology could enhance the preservation of critical vascular structures and nerves, thereby minimizing post-operative complications.

NIR-IIb Fluorescence Molecular Tomography of Glioblastomas Based on Heterogeneous Mouse Models and Adaptive Projection Match Pursuit Method.

Fluorescence molecular tomography (FMT) using the second near-infrared window (NIR-II) fluorescence has been proved to outperform conventional FMT using the first near-infrared window (NIR-I) fluorescence. However, it was still a challenge to achieve a satisfactory reconstructed light source using NIR-II FMT as the NIR-IIa (1300-1400 nm) fluorescence in the NIR-II spectrum used in the previous NIR-II FMT study was still suffering from prominent absorption and scattering of tissue. A novel NIR-IIb (1500-1700 nm) FMT method was proposed and applied in the reconstruction of glioblastomas in animal models. Optical parameters that describe the effect of different tissue on the NIR-IIb photons were calculated to construct a light propagation model of NIR-IIb light to form the forward model. Besides, a novel adaptive projection matching pursuit (APMP) method was further adopted to accurately solve the inverse problem. Location error and Dice coefficient were used to evaluate the accuracy of reconstruction. Simulation experiments using single-source and dual-source and in vivo experiments were conducted to evaluate the reconstructed light source. Both simulation and in vivo experiments demonstrated that NIR-IIb FMT achieved reduced location error and improved shape similarity compared with NIR-IIa. NIR-IIb FMT of orthotopic mouse models further achieved a location error of 0.65 mm and a Dice coefficient of 0.56, which both outperformed NIR-IIa. The results demonstrated that NIR-IIb has better reconstruction performance for positioning accuracy and shape recovery. The inspiring results in this study demonstrate the effectiveness and advantages of NIR-IIb FMT in precise tumor positioning.

Deep compression network for enhancing numerical reconstruction quality of full-complex holograms.

The field of digital holography has been significant developed in recent decades, however, the commercialization of digital holograms is still hindered by the issue of large data sizes. Due to the complex signal characteristics of digital holograms, which are of interferometric nature, traditional codecs are not able to provide satisfactory coding efficiency. Furthermore, in a typical coding scenario, the hologram is encoded and then decoded, leading to a numerical reconstruction via a light wave propagation model. While previous researches have mainly focused on the quality of the decoded hologram, it is the numerical reconstruction that is visible to the viewer, and thus, its quality must also be taken into consideration when designing a coding solution. In this study, the coding performances of existing compression standards, JPEG2000 and HEVC-Intra, are evaluated on a set of digital holograms, then the limitations of these standards are analyzed. Subsequently, we propose a deep learning-based compression network for full-complex holograms that demonstrates superior coding performance when compared to the latest standard codecs such as VVC and JPEG-XL, in addition to JPEG2000 and HEVC. The proposed network incorporates not only the quality of the decoded hologram, but also the quality of the numerical reconstruction as distortion costs for network training. The experimental results validate that the proposed network provides superior objective coding efficiency and better visual quality compared to the existing methods.

PECULIARITIES OF THE FORMATION OF SCINCILLATION RESPONSE IN ORGANIC MATERIALS WITH STOCHASTIC CHARACTER OF LIGHT PROPAGATION

The paper examines the possibility of using an organic polycrystal as the “opaque” scintillator. Polycrystals are produced by pressing crystalline grains. When light propagates through a polycrystal, it is repeatedly reflected and refracted at the boundaries of the grains. This makes its propagation difficult. We studied the light output and optical transmittance of stilbene and p-terphenyl polycrystals with different fractions of crystalline grain: from 0.06…0.1 to 2.0…2.5 mm (the samples 20 mm in diameter and 2 mm in height) was conducted. Modelling of light propagation in polycrystalline samples of stilbene and p-terphenyl was carried out and the values of the light collection coefficients were calculated. It was found that in order to obtain the polycrystalline samples with sufficiently high light output and high efficiency of detection of local sites of interaction of ionizing radiations, grains in the range of 0.4…0.8 mm should be used.

Numerical study of light scattering and propagation in soymilk: Effects of particle size distributions, concentrations, and medium sizes

Understanding light scattering and propagation in soymilk is crucial for the nondestructive evaluation of food qualities of soymilk and its products by near-infrared spectroscopy. We aim to numerically examine light scattering and propagation in soymilk samples with different solid-content properties (particle size distributions and concentrations) and medium sizes. Using the dependent scattering theory, we calculated the light-scattering properties of the samples on the sub-nanometer scale. In contrast, we calculated light propagation on the millimeter scale using the radiative transfer theory. Our numerical results show that the scattering properties of soymilk strongly depend on the solid-content properties, implying a high correlation between the solid-content and scattering properties. The sample with the largest mean diameter and broadest size distribution has the smallest reduced scattering coefficient among the samples, although it has the largest scattering coefficient. We compared the reflected fluence rates in a cylinder and semi-infinite medium. The comparative study shows the finite size effect on light propagation can be negligible at the cylinder radius of more than 2.0 cm. In that case, the light propagation model for a semi-infinite medium is a better choice to provide fast calculations in inverse analysis on the scattering properties. Our findings are essential for further developing near-infrared spectroscopy using scattered light and the physics of light scattering in dense polydisperse media.

Laser–tissue interaction simulation considering skin-specific data to predict photothermal damage lesions during laser irradiation

Abstract This study aimed to develop a simulation model that accounts for skin-specific properties in order to predict photothermal damage during skin laser treatment. To construct a computational model, surface geometry information was obtained from an optical coherence tomography image, and the absorption coefficient of the skin was determined through spectrophotometry. The distribution of the internal light dose inside the skin medium was calculated using the light propagation model based on the Monte Carlo method. The photothermal response due to the absorption of laser light was modeled by a finite difference time domain model to solve the bio-heat transfer equation. The predicted depth and area of the damaged lesions from the simulation model were compared to those measured in ex vivo porcine skin. The present simulation model gave acceptable predictions with differences of approximately ∼10% in both depth and area.

Design of computer-generated holograms based on semi-rigorous simulations of sub-wavelength structures.

Conventional design methods for computer-generated holograms often rely on the scalar diffraction theory because the calculation effort of rigorous simulations is too high. But for sub-wavelength lateral feature sizes or large deflection angles, the performance of realized elements will show distinct deviations from the expected scalar behavior. We propose a new design method that overcomes this issue by incorporating high-speed semi-rigorous simulation techniques that allow the modeling of light propagation at an accuracy close to the rigorous methods. This includes an approach to solve the inverse problem of calculating a geometric structure that is able to form a certain physical field distribution.

Investigating learning-empowered hologram generation for holographic displays with ill-tuned hardware.

Existing computational holographic displays often suffer from limited reconstruction image quality mainly due to ill-conditioned optics hardware and hologram generation software. In this Letter, we develop an end-to-end hardware-in-the-loop approach toward high-quality hologram generation for holographic displays. Unlike other hologram generation methods using ideal wave propagation, ours can reduce artifacts introduced by both the light propagation model and the hardware setup, in particular non-uniform illumination. Experimental results reveal that, compared with classical computer-generated hologram algorithm counterparts, better quality of holographic images can be delivered without a strict requirement on both the fine assembly of optical components and the good uniformity of laser sources.