Turbid Media Research Articles

Measurement of turbid media by total internal reflection with Goos-Hänchen angle displacement

Based on the Goos-Hänchen effect and the intensity attenuation of the evanescent wave penetrating and traveling, a new theoretical model of total internal reflection from the interface of turbid media is proposed and an analytical reflectance expression in a wide incident angle range is developed. The Goos-Hänchen angle displacement between the critical reflectance and the saturated reflectance is discovered. A sensor, for measuring the complex refractive index of turbid media in real-time, with divergent light source is designed. The captured images show that the light distribution reflected from the transparent medium has a sharp boundary, but for turbid media, the reflected light intensity attenuates during the transition from total to non-total internal reflection regions. It is successful and accurate that the new model fits the experimental data of the reflectance and the complex refractive index of turbid media is measured by our sensor. The results show that measuring has advantages in real-time, in situ, and with high accuracy.

Twists through turbidity: propagation of light carrying orbital angular momentum through a complex scattering medium

We explore the propagation of structured vortex laser beams-shaped light carrying orbital angular momentum (OAM)-through complex multiple scattering medium. These structured vortex beams consist of a spin component, determined by the polarization of electromagnetic fields, and an orbital component, arising from their spatial structure. Although both spin and orbital angular momenta are conserved when shaped light propagates through a homogeneous, low-scattering medium, we investigate the conservation of these angular momenta during the propagation of Laguerre–Gaussian (LG) beams with varying topological charges through a turbid multiple scattering environment. Our findings demonstrate that the OAM of the LG beam is preserved, exhibiting a distinct phase shift indicative of the ‘twist of light’ through the turbid medium. This preservation of OAM within such environments is confirmed by in-house developed Monte Carlo simulations, showing strong agreement with experimental studies. Our results suggest exciting prospects for leveraging OAM in sensing applications, opening avenues for groundbreaking fundamental research and practical applications in optical communications and remote sensing.

Ultralow radiant exposure of a short-pulsed laser to disrupt melanosomes with localized thermal damage through a turbid medium

Short-pulsed lasers can treat dermal pigmented lesions through selective photothermolysis. The irradiated light experiences multiple scattering by the skin and is absorbed by abnormal melanosomes as well as by normal blood vessels above the target. Because the fluence is extremely high, the absorbed light can cause thermal damage to the adjacent tissue components, leading to complications. To minimize radiant exposure and reduce the risk of burns, a model of the melanosome-disruption threshold fluence (MDTF) has been developed that accounts for the light-propagation efficiency in the skin. However, the light-propagation efficiency is attenuated because of multiple scattering, which limits the extent to which the radiant exposure required for treatment can be reduced. Here, this study demonstrates the principle of melanosome disruption with localized thermal damage through a turbid medium by ultralow radiant exposure of a short-pulsed laser. The MDTF model was combined with a wavefront-shaping technique to design an irradiation condition that can increase the light-propagation efficiency to the target. Under this irradiation condition, melanosomes were disrupted at a radiant exposure 25 times lower than the minimal value used in conventional laser treatments. Furthermore, almost no thermal damage to the skin was confirmed through a numerical simulation. These experimental and numerical results show the potential for noninvasive melanosome disruption and may lead to the improvement of the safety of short-pulsed laser treatment.

Boundary-layer structures arising in linear transport theory.

We consider boundary-layer structures that arise in connection with the transport of neutral particles (e.g., photons or neutrons) through a participating medium. Such boundary-layer structures were previously identified by the authors in certain particular cases [Phys. Rev. E 104, L032801 (2021)2470-004510.1103/PhysRevE.104.L032801]. Extending the previous work to anisotropic scattering and general Fresnel boundary conditions, this contribution presents computational algorithms which (1) resolve the aforementioned layers as well as previously unreported boundary layers associated with Fresnel boundary transmission and reflection, and (2) yield accurate simulations at fixed computational cost for transport under phase functions with arbitrarily strong anisotropy. The present paper additionally includes (3) Mathematical proofs which justify the numerical methods proposed for resolution of boundary-layer structures. The impact of the new theory on algorithmic performance is demonstrated through a series of 1D computational benchmarks that emulate typical photon- and neutron-transport applications such as, e.g., optical tomography, and nuclear reactor analysis and design. Experimental results for transmission of photons through turbid media are presented, exhibiting close agreement between simulated and experimental data. As illustrated by means of a variety of numerical results, the proposed boundary-layer-based approach tackles transport problems with unprecedented accuracy and efficiency.

Physically based modelling of spectral transmittance through forest canopies

Abstract Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance. The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe. In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests. We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.

Wide-field imaging and recognition through cascaded complex scattering media

Considering the obvious application value in the field of minimally invasive and non-destructive clinical healthcare, we explore the challenge of wide-field imaging and recognition through cascaded complex scattering media, a topic that has been less researched, by realizing wide-field imaging and pathological screening through multimode fibers (MMF) and turbid media. To address the challenge of extracting features from chaotic and globally correlated speckles formed by transmitting images through cascaded complex scattering media, we establish a deep learning approach based on SMixerNet. By efficiently using the parameter-free matrix transposition, SMixerNet achieves a broad receptive field with less inductive bias through concise multi-layer perceptron (MLP). This approach circumvents the parameter's intensive requirements of previous implementations relying on self-attention mechanisms for global receptive fields. Imaging and pathological screening results based on extensive datasets demonstrate that our approach achieves better performance with fewer learning parameters, which helps deploy deep learning models on desktop-level edge computing devices for clinical healthcare. Our research shows that, deep learning facilitates imaging and recognition through cascaded complex scattering media. This research extends the scenarios of medical and industrial imaging, offering additional possibilities in minimally invasive and non-destructive clinical healthcare and industrial monitoring in harsh and complex scenarios.

Features of Light Scattering in Turbid Media As Modeled for Two-Component Emulsions

Features of Light Scattering in Turbid Media As Modeled for Two-Component Emulsions

Reconstruction of Optical Properties in Turbid Media: Omitting the Need of the Collimated Transmission for an Integrating Sphere Setup.

Currently, the most reliable approach to reconstruct optical properties, namely absorption coefficient, reduced scattering coefficient, scattering coefficient and asymmetry factor, of turbid media is through inverse Monte Carlo simulation. To determine these optical properties, three measurements are required: total transmission, total reflection and collimated transmission. However, the accurate determination of the collimated transmission is very difficult. To overcome the difficulty of measuring the collimated transmission, it is proposed to measure the total transmission and total reflection of the same sample with two different thicknesses instead. To prove this alternative solution, machine learning is used to prove that the repeated measurement for two different thicknesses carries all the necessary information. As a result, all four optical properties can be measured with high accuracy, particularly for interpolation problems where the test data fall within the range of the training data. For extrapolation problems, high accuracy can be achieved in the determination of at least the absorption coefficient, reduced scattering coefficient and scattering coefficient. Hence, these results allow that in the future, an easier and therefore more precise reconstruction of the optical properties is possible, potentially even with inverse Monte Carlo simulations as the current standard.

Exploring the origin of stronger survival of polarized vortex beams through scattering media

Laguerre–Gaussian (LG) beams carrying orbital angular momentum (OAM) have shown promise in deep tissue imaging, medical diagnostics, and optical communication due to their robust propagation properties through scattering media. The insight on the mechanism for stronger survival of OAM carrying beam in tissue-like turbid media is expected to contribute towards a better understanding of light transport in the presence of scattering, as well as guide optimization of the intensity, phase, and polarization structure of light for use in biomedical applications like in tissue imaging. We examine the scattering properties by studying the propagation of polarized vortex beams transmitted through tissue-like turbid scattering media. We demonstrate that the intensity profile has a much more profound effect on depolarization than the phase profile for LG beams. Our results indicate that the observed stronger propagation for the higher-order LG beams is due to a higher anisotropy factor g, as seen by the incident beam. We have performed the degree of polarization measurements for the forward scattered light in the case of both LG beams and perfect vortex beams with varying topological charges. A comparison between the observed depolarization trends for the two classes of OAM-carrying beams suggests that the robust scattering properties of the LG beams originate from the intensity profile while the phase profile does not seem to play a major role in the stronger survival of OAM-carrying beam in turbid media.

Unwrapping non-locality in the image transmission through turbid media

Achieving high-fidelity image transmission through turbid media is a significant challenge facing both the AI and photonic/optical communities. While this capability holds promise for a variety of applications, including data transfer, neural endoscopy, and multi-mode optical fiber-based imaging, conventional deep learning methods struggle to capture the nuances of light propagation, leading to weak generalization and limited reconstruction performance. To address this limitation, we investigated the non-locality present in the reconstructed images and discovered that conventional deep learning methods rely on specific features extracted from the training dataset rather than meticulously reconstructing each pixel. This suggests that they fail to effectively capture long-range dependencies between pixels, which are crucial for accurate image reconstruction. Inspired by the physics of light propagation in turbid media, we developed a global attention mechanism to approach this problem from a broader perspective. Our network harnesses information redundancy generated by peculiar non-local features across the input and output fiber facets. This mechanism enables a two-order-of-magnitude performance boost and high fidelity to the data context, ensuring an accurate representation of intricate details in a pixel-to-pixel reconstruction rather than mere loss minimization.

Picosecond Imaging Of Bubbly Flows

Despite their ubiquity, bubbly flows are notoriously challenging to instrument. In high-void fractions, most optical measurement techniques fail due to intense scattering and occlusion. Ultrafast imaging with picosecond exposure times offers an opportunity, allowing measurements before scattering obscures the field of view. Furthermore, detecting the time of arrival of photons allows measurement of bubble sizes, even for those partially occluded. Through implementing the Optical Kerr Effect (OKE), using a 70 fs 800 nm pulsed laser and a Carbon Disulfide cell, images are acquired with an exposure of 2.9 ps. Gating these images with an optical delay stage further allows the decomposition of scenes with 33 fs time increments. Proof of concept is demonstrated by applying this approach to static scenes of submerged bubbles at low and high void fractions. The experimental temporal decomposition shows the arrival of photons that travel through different media (air, water, and glass) and those scattered within the turbid media. A new visualization is presented to display this breakdown concisely, the Temporally Tagged Shadowgraph (TTS), presenting the time of arrival with the context of position and surroundings. Bubble sizes are measured using this temporal information and through edge detection, with good agreement for single unoccluded bubbles. With polydisperse bubble plumes, time delay-based measurements tend to overestimate bubble diameter; however, this could also be due to greater uncertainty in edge detection with occlusion. This demonstrates the potential of Ultrafast Imaging when it comes to probing high-void fraction flows. By further shortening exposure times and improving the robustness of processing algorithms, the scope of applicability of this technique can be widened.

Enhanced genetic algorithm through scattering media by searching-strategy optimization

Feedback-based wavefront shaping techniques are efficient approaches to compensate for multiple scattering and achieve desired focuses inside or through turbid media. In this paper, we introduced the negative linear (NL) hypothesis as the criterion to optimize the feedback-based optimization approaches, such as genetic algorithm (GA). This hypothesis reveals that the key to improving the parameters to their optima lies in acquiring maximum initial convergence speed and maintaining a negative linear decline thereafter. Based on the hypothesis, an efficient genetic algorithm based on searching-strategy optimization (GA-SSO), which applies dual adaptive control of mutation and fixed linear fitness scaling selection, was proposed to accelerate the convergence speed. The simulations and experiments confirmed that NL is a basic guide to optimize the parameters to the optima of feedback-based wavefront shaping techniques.

Towards a sensing model using a random laser combined with diffuse reflectance spectroscopy.

The previous research proves that the random laser emission reflects not only the scattering properties but also the absorption properties. The random laser is therefore considered a potential tool for optical properties sensing. Although the qualitative sensing using the random laser is extensively investigated, a quantitative measurement of optical properties is still rare. In this study, a generalized mathematical quantitative model using random laser combined with diffuse reflectance spectroscopy is proposed for optical sensing in turbid media. This model describes the gain effect of the active medium and the optical properties effect of the passive medium separately. Rhodamine 6G is used as the active medium. Intralipid and ink are employed to demonstrate the effect of the scattering and absorption, respectively. The peak wavelength shift of the random laser is proved to be an ideal sensing parameter for this sensing model. It is also revealed that the scaling parameters in the sensing model are interrelated and can be simplified to one. With this combined model, the direct sensing of optical properties in diverse turbid media is promising.

Mueller matrix polarimetry approach for noninvasive glucose sensing with absorbance and anisotropic parameters on fingertips.

A Mueller matrix polarimetry system at 532 nm wavelength is developed for noninvasive glucose sensing in turbid media such as human's fingertip. The system extracts mean absorbance and anisotropic properties, demonstrated numerically and experimentally with phantom glucose samples. It is found that mean absorbance ( ), depolarization index (Δ), and linear dichroism (LD) show linear variation with glucose concentration 100-500 mg/dL. In addition, LightTools simulations indicate proportional scaling of scattering effects with , Δ, and LD. Real-world tests on fingertip show a strong correlation between these properties and blood glucose levels with a mean absolute relative deviation (MARD) of 12.56% and a correlation coefficient (R2) of 0.875 in prediction by a neural network (NN) model, highlighting the advantages of Mueller matrix in extracting more parameters related to blood glucose.

Depolarization diagrams for circularly polarized light scattering for biological particle monitoring.

The depolarization of circularly polarized light (CPL) caused by scattering in turbid media reveals structural information about the dispersed particles, such as their size, density, and distribution, which is useful for investigating the state of biological tissue. However, the correlation between depolarization strength and tissue parameters is unclear. We aimed to examine the generalized correlations of depolarization strength with the particle size and wavelength, yielding depolarization diagrams. The correlation between depolarization intensity and size parameter was examined for single and multiple scattering using the Monte Carlo simulation method. Expanding the wavelength width allows us to obtain depolarization distribution diagrams as functions of wavelength and particle diameter for reflection and transparent geometries. CPL suffers intensive depolarization in a single scattering against particles of various specific sizes for its wavelength, which becomes more noticeable in the multiple scattering regime. The depolarization diagrams with particle size and wavelength as independent variables were obtained, which are particularly helpful for investigating the feasibility of various particle-monitoring methods. Based on the obtained diagrams, several applications have been proposed, including blood cell monitoring, early embryogenesis, and antigen-antibody interactions.

Estimation of canopy photon recollision probability from airborne laser scanning

The past two decades have witnessed the widespread application of the spectral invariant theory (p-theory) in modeling the shortwave radiation absorbed or scattered by vegetation. The basic principle of this theory is that the canopy reflectance is solely determined by the optical properties of leaves and the photon recollision probability p. As one of the spectral invariants, p serves as a critical bond between the spectral characteristics of the canopy at any wavelength and the reflectance, transmittance, or absorptance of the vegetation canopy, and is intimately associated with the solution of the radiative transfer equation. Currently, estimation of p-value is predominantly accomplished through canopy structural parameters, such as the spherically averaged silhouette to total area ratio (STAR¯) or Leaf Area Index (LAI). In this work, we provide an approach to estimate canopy photon recollision probability directly from Airborne Laser Scanning (ALS) data. We showed that the recollision probability can be interpreted as the interceptions of virtual rays emitted from sampling points within tree crowns described with turbid media, which avoided detailed reconstruction of leaf structures. The approach was evaluated with both virtual experiments as well as field measurements. The virtual experiments employed the Large-scalE remote Sensing data and image Simulation model (LESS) to simulate virtual ALS scanning data based on three RAdiation transfer Model Intercomparison (RAMI) actual canopies, with each divided into 25 segments, for which the p-values could be obtained through LESS. Our findings showed that p-values can be accurately estimated from ALS point clouds. Specifically, it exhibited great consistency with RMSE% less than 5% for broadleaved forest scenes, 25% for mixed forest scenes, and less than 10% for coniferous forest scenes in virtual experiments, respectively, and less than 25% compared to field measurements. This study displays the potential of ALS as a promising tool for forest structure characterization and short-wave radiation modeling in forest ecosystems.

Harmonic Imaging of Stem Cells in Whole Blood at GHz Pixel Rate.

The pre-clinical validation of cell therapies requires monitoring the biodistribution of transplanted cells in tissues of host organisms. Real-time detection of these cells in the circulatory system and identification of their aggregation state is a crucial piece of information, but necessitates deep penetration and fast imaging with high selectivity, subcellular resolution, and high throughput. In this study, multiphoton-based in-flow detection of human stem cells in whole, unfiltered blood is demonstrated in a microfluidic channel. The approach relies on a multiphoton microscope with diffractive scanning in the direction perpendicular to the flow via a rapidly wavelength-swept laser. Stem cells are labeled with metal oxide harmonic nanoparticles. Thanks to their strong and quasi-instantaneous second harmonic generation (SHG), an imaging rate in excess of 10 000 frames per second is achieved with pixel dwell times of 1 ns, a duration shorter than typical fluorescence lifetimes yet compatible with SHG. Through automated cell identification and segmentation, morphological features of each individual detected event are extracted and cell aggregates are distinguished from isolated cells. This combination of high-speed multiphoton microscopy and high-sensitivity SHG nanoparticle labeling in turbid media promises the detection of rare cells in the bloodstream for assessing novel cell-basedtherapies.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Modelling polarized pulsed LiDAR signals in scattering media: an approach for optimizing systems in real-world scenarios

Adverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy. In such conditions, advanced simulation methods enable the testing of different experimental configurations and the determination of the best possible setup. With this purpose, a Monte Carlo algorithm is presented for modeling polarized pulsed LiDAR signals in turbid media. This algorithm is designed for immediate applicability, incorporating realistic media characterization and accounting for the attributes of existing prototypes. It allows testing various experimental configurations, managing optical obstacles, adjusting polarization arrangements, and different geometries of particles within the medium. The developed algorithm accurately characterizes backscattering signals, revealing their dependence on medium properties. A relationship between visibility and backscattering energy is identified, offering insights for sensor optimization. Polarization analysis highlights the efficacy of circular polarization in mitigating scattering effects and establishes a connection with the polarimetric characteristics of imaged targets. The algorithm's application to irregular particles reveals also an unexpected behavior of polarized light, challenging established strategies. These diverse use cases exemplify the algorithm's capability to model real-world circumstances, emphasizing its significance in predicting cutting-edge situations when designing optical systems for complex and demanding outdoor scenarios.

Study of Time-Resolved Dynamics in Turbid Medium Using a Single-Cavity Dual-Comb Laser.

In measuring cerebral blood flow (CBF) noninvasively using optical techniques, diffusing-wave spectroscopy is often combined with near-infrared spectroscopy to obtain a reliable blood flow index. Measuring the blood flow index at a determined depth remains the ultimate goal. In this study, we present a simple approach using dual-comb lasers where we simultaneously measure the absorption coefficient (μa), the reduced scattering coefficient (μs '), and dynamic properties. This system can also effectively differentiate dynamics from various depths, which is crucial for analyzing multilayer dynamics. For CBF measurements, this capability is particularly valuable as it helps mitigate the influence of the scalp and skull, thereby enhancing the specificity of deep tissue.