Tunable nonlinear optical mapping in a multiple-scattering cavity.

Nonlinear disordered systems are not only a model system for fundamental studies but also in high demand for practical applications. However, optical nonlinearity based on intrinsic material response is weak in random scattering systems. Here, we propose and experimentally realize a highly nonlinear mapping between the scattering potential and the emerging light of a reconfigurable multiple-scattering cavity. A quantitative analysis of the degree of nonlinearity reveals its dependence on the number of scattering events. The effective order of nonlinear mapping can be tuned over a wide range at low optical lower. The strong nonlinear mapping enhances output intensity fluctuations and long-range correlations. The flexibility, robustness, and energy efficiency of our approach provides a versatile platform for exploring such nonlinear mappings for various applications.

Embedded Spatial-Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification.

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial-temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial-temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.

Restoration of Binocular Images Degraded by Optical Scattering through Estimation of Atmospheric Coefficients.

A binocular vision-based approach for the restoration of images captured in a scattering medium is presented. The scene depth is computed by triangulation using stereo matching. Next, the atmospheric parameters of the medium are determined with an introduced estimator based on the Monte Carlo method. Finally, image restoration is performed using an atmospheric optics model. The proposed approach effectively suppresses optical scattering effects without introducing noticeable artifacts in processed images. The accuracy of the proposed approach in the estimation of atmospheric parameters and image restoration is evaluated using synthetic hazy images constructed from a well-known database. The practical viability of our approach is also confirmed through a real experiment for depth estimation, atmospheric parameter estimation, and image restoration in a scattering medium. The results highlight the applicability of our approach in computer vision applications in challenging atmospheric conditions.

A Fiber Optic Sensor for Monitoring the Spectral Alterations and Depth in Ex Vivo and In Vivo Cryosurgery.

This article discusses how to monitor the freezing depth during cryotherapy using a fiber optic array sensor. The sensor was used to measure the backscattered and transmitted light from frozen and unfrozen ex vivo porcine tissue and in vivo human skin tissue (finger). The technique exploited the variations in optical diffusion properties of the frozen and unfrozen tissues to determine the extent of freezing. Ex vivo and in vivo measurements yielded comparable results, despite spectral variations attributable to the hemoglobin absorption peak in the human frozen and unfrozen tissues. However, because the spectral fingerprints of the freeze-thaw process in the ex vivo and in vivo experiments were similar, we could extrapolate the maximum depth of freezing. Therefore, this sensor has the potential to be utilized for monitoring cryosurgery in real time.

Structured illumination microscopy artefacts caused by illumination scattering.

Despite its wide application in live-cell super-resolution (SR) imaging, structured illumination microscopy (SIM) suffers from aberrations caused by various sources. Although artefacts generated from inaccurate reconstruction parameter estimation and noise amplification can be minimized, aberrations due to the scattering of excitation light on samples have rarely been investigated. In this paper, by simulating multiple subcellular structure with the distinct refractive index from water, we study how different thicknesses of this subcellular structure scatter incident light on its optical path of SIM excitation. Because aberrant interference light aggravates with the increase in sample thickness, the reconstruction of the 2D-SIM SR image degraded with the change of focus along the axial axis. Therefore, this work may guide the future development of algorithms to suppress SIM artefacts caused by scattering in thick samples. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Upconversion Spectral Rulers for Transcutaneous Displacement Measurements.

We describe a method to measure micron to millimeter displacement through tissue using an upconversion spectral ruler. Measuring stiffness (displacement under load) in muscles, bones, ligaments, and tendons is important for studying and monitoring healing of injuries. Optical displacement measurements are useful because they are sensitive and noninvasive. Optical measurements through tissue must use spectral rather than imaging approaches because optical scattering in the tissue blurs the image with a point spread function typically around the depth of the tissue. Additionally, the optical measurement should have low background and minimal intensity dependence. Previously, we demonstrated a spectral encoder using either X-ray luminescence or fluorescence, but the X-ray luminescence required an expensive X-ray source and used ionizing radiation, while the fluorescence sensor suffered from interference from autofluorescence. Here, we used upconversion, which can be provided with a simple fiber-coupled spectrometer with essentially autofluorescence-free signals. The upconversion phosphors provide a low background signal, and the use of closely spaced spectral peaks minimizes spectral distortion from the tissue. The small displacement noise level (precision) through tissue was 2 µm when using a microscope-coupled spectrometer to collect light. We also showed proof of principle for measuring strain on a tendon mimic. The approach provides a simple method to study biomechanics using implantable sensors.

Enhanced coupling of light into a turbid medium through microscopic interface engineering.

There are many optical detection and sensing methods used today that provide powerful ways to diagnose, characterize, and study materials. For example, the measurement of spontaneous Raman scattering allows for remote detection and identification of chemicals. Many other optical techniques provide unique solutions to learn about biological, chemical, and even structural systems. However, when these systems exist in a highly scattering or turbid medium, the optical scattering effects reduce the effectiveness of these methods. In this article, we demonstrate a method to engineer the geometry of the optical interface of a turbid medium, thereby drastically enhancing the coupling efficiency of light into the material. This enhanced optical coupling means that light incident on the material will penetrate deeper into (and through) the medium. It also means that light thus injected into the material will have an enhanced interaction time with particles contained within the material. These results show that, by using the multiple scattering of light in a turbid medium, enhanced light-matter interaction can be achieved; this has a direct impact on spectroscopic methods such as Raman scattering and fluorescence detection in highly scattering regimes. Furthermore, the enhanced penetration depth achieved by this method will directly impact optical techniques that have previously been limited by the inability to deposit sufficient amounts of optical energy below or through highly scattering layers.

Digital design of multimaterial photonic particles.

Scattering of light from dielectric particles whose size is on the order of an optical wavelength underlies a plethora of visual phenomena in nature and is a foundation for optical coatings and paints. Tailoring the internal nanoscale geometry of such "photonic particles" allows tuning their optical scattering characteristics beyond those afforded by their constitutive materials-however, flexible yet scalable processing approaches to produce such particles are lacking. Here, we show that a thermally induced in-fiber fluid instability permits the "digital design" of multimaterial photonic particles: the precise allocation of high refractive-index contrast materials at independently addressable radial and azimuthal coordinates within its 3D architecture. Exploiting this unique capability in all-dielectric systems, we tune the scattering cross-section of equisized particles via radial structuring and induce polarization-sensitive scattering from spherical particles with broken internal rotational symmetry. The scalability of this fabrication strategy promises a generation of optical coatings in which sophisticated functionality is realized at the level of the individual particles.

Demonstration of a Speckle Based Sensing with Pulse-Doppler Radar for Vibration Detection.

In previous works, an optical technique for extraction and separation of remote static vibrations has been demonstrated. In this paper, we will describe an approach in which RF speckle movement is used to extract remote vibrations of a static target. The use of conventional radar Doppler methods is not suitable for detecting vibrations of static targets. In addition, the speckle method has an important advantage, in that it is able to detect vibrations at far greater distances than what is normally detected in classical optical methods. The experiment described in this paper was done using a motorized vehicle, which engine was turned on and off. The results showed that the system was able to distinguish between the different engine states, and in addition, was able to determine the vibration frequency of the engine. The first step towards real time detection of human vital signs using RF speckle patterns is presented.

Stromal alterations in ovarian cancers via wavelength dependent Second Harmonic Generation microscopy and optical scattering.

BACKGROUND: Ovarian cancer remains the most deadly gynecological cancer with a poor aggregate survival rate; however, the specific rates are highly dependent on the stage of the disease upon diagnosis. Current screening and imaging tools are insufficient to detect early lesions and are not capable of differentiating the subtypes of ovarian cancer that may benefit from specific treatments. METHOD: As an alternative to current screening and imaging tools, we utilized wavelength dependent collagen-specific Second Harmonic Generation (SHG) imaging microscopy and optical scattering measurements to probe the structural differences in the extracellular matrix (ECM) of normal stroma, benign tumors, endometrioid tumors, and low and high-grade serous tumors. RESULTS: The SHG signatures of the emission directionality and conversion efficiency as well as the optical scattering are related to the organization of collagen on the sub-micron size scale and encode structural information. The wavelength dependence of these readouts adds additional characterization of the size and distribution of collagen fibrils/fibers relative to the interrogating wavelengths. We found a strong wavelength dependence of these metrics that are related to significant structural differences in the collagen organization and are consistent with the dualistic classification of type I and II serous tumors. Moreover, type I endometrioid tumors have strongly differing ECM architecture than the serous malignancies. The SHG metrics and optical scattering measurements were used to form a linear discriminant model to classify the tissues, and we obtained high accuracy (>90%) between high-grade serous tumors from the other tissue types. High-grade serous tumors account for ~70% of ovarian cancers, and this delineation has potential clinical applications in terms of supplementing histological analysis, understanding the etiology, as well as development of an in vivo screening tool. CONCLUSIONS: SHG and optical scattering measurements provide sub-resolution information and when combined provide superior diagnostic power over clinical imaging modalities. Additionally the measurements are able to delineate the different subtypes of ovarian cancer and may potentially assist in treatment protocols. Understanding the altered collagen assembly can supplement histological analysis and provide new insight into the etiology. These methods could become an in vivo screening tool for earlier detection which is important since ovarian malignancies can metastasize while undetectable by current clinical imaging resolution.

Skeletal light-scattering accelerates bleaching response in reef-building corals.

BACKGROUND: At the forefront of ecosystems adversely affected by climate change, coral reefs are sensitive to anomalously high temperatures which disassociate (bleaching) photosynthetic symbionts (Symbiodinium) from coral hosts and cause increasingly frequent and severe mass mortality events. Susceptibility to bleaching and mortality is variable among corals, and is determined by unknown proportions of environmental history and the synergy of Symbiodinium- and coral-specific properties. Symbiodinium live within host tissues overlaying the coral skeleton, which increases light availability through multiple light-scattering, forming one of the most efficient biological collectors of solar radiation. Light-transport in the upper ~200 µm layer of corals skeletons (measured as 'microscopic' reduced-scattering coefficient, µ'(S,m)), has been identified as a determinant of excess light increase during bleaching and is therefore a potential determinant of the differential rate and severity of bleaching response among coral species. RESULTS: Here we experimentally demonstrate (in ten coral species) that, under thermal stress alone or combined thermal and light stress, low-µ'(S,m) corals bleach at higher rate and severity than high-µ'(S,m) corals and the Symbiodinium associated with low-µ'(S,m) corals experience twice the decrease in photochemical efficiency. We further modelled the light absorbed by Symbiodinium due to skeletal-scattering and show that the estimated skeleton-dependent light absorbed by Symbiodinium (per unit of photosynthetic pigment) and the temporal rate of increase in absorbed light during bleaching are several fold higher in low-µ'(S,m) corals. CONCLUSIONS: While symbionts associated with low-[Formula: see text] corals receive less total light from the skeleton, they experience a higher rate of light increase once bleaching is initiated and absorbing bodies are lost; further precipitating the bleaching response. Because microscopic skeletal light-scattering is a robust predictor of light-dependent bleaching among the corals assessed here, this work establishes µ'(S,m) as one of the key determinants of differential bleaching response.

Optical properties of tissues quantified using morphological granulometry from phase-contrast images of thin tissue samples.

A method combining morphological granulometry with Mie theory to determine optical scattering in biological tissues was proposed. Otsu's method was applied to binarize phase-contrast images. Binary morphological granulometry was used to estimate size density distribution of the tissue samples based on the binary phase-contrast images. Our results showed that the optical parameters associated with light scattering in tissue could be quantitatively determined by combining size density distribution with Mie theory. It was suggested that this unique method could be used to characterize biological tissues for disease diagnosis.

Light Scattering Method for Aerosol Sizing Based on Machine Learning.

Optical scattering has been widely used for aerosol sizing due to its noninvasive and real-time measurement. However, it is crucial to retrieve the particle size distribution (PSD) of aerosols without prior knowledge of the refractive index. Now, it has been a great challenge to measure the refractive index in situ. In this study, a novel PSD sensing method utilizing the light scattering angular spectrum (LSAS) and machine learning techniques is proposed to address this challenge. The complex nonlinear relationship between LSAS and PSD can be constructed while accounting for the refractive index of aerosols. A miniaturized prototype sensor is designed and tested on different sizes of aerosol samples. The experiment results showed that the maximum Kullback-Leibler divergence (DKL) of PSD is 0.07, which indicates that the sensing method can provide the ability for highly accurate aerosol PSD measurement without requiring prior knowledge of the refractive index. The compacted prototype sensor shows great potential for aerosol analysis in conventional field measurements outside the laboratory.

Rapid alignment-free bacteria identification via optical scattering with LEDs and YOLOv8.

Rapid and accurate bacterial identification is essential for timely treatment of infections like sepsis. While traditional methods are reliable, they lack speed, and advanced molecular techniques often suffer from cost and complexity. The bacterial detection technology based on optical scattering system offers a rapid, label-free alternative but traditionally relies on complex lasers and analysis. Our enhanced approach utilizes RGB light emitting diodes (LEDs) as the light source. Three diffraction images of bacterial colonies from different LED colors are separately captured by a USB camera and combined using an image registration algorithm to enhance image sharpness. Our approach utilizes an object detection model, i.e., YOLOv8, for analysis achieving high-accuracy differentiation between bacterial strains. We demonstrate the effectiveness of this approach, achieving an average accuracy of 97% (mAP50 of 0.97), including accurate discrimination of closely related strains and the significant pathogen Staphylococcus aureus MRSA 1320. Our enhancement offers advantages in affordability, usability, and seamless integration into existing workflows, providing an alternative for rapid bacterial identification.

Convolutional neural network-based regression analysis to predict subnuclear chromatin organization from two-dimensional optical scattering signals.

Significance: Azimuth-resolved optical scattering signals obtained from cell nuclei are sensitive to changes in their internal refractive index profile. These two-dimensional signals can therefore offer significant insights into chromatin organization. Aim: We aim to determine whether two-dimensional scattering signals can be used in an inverse scheme to extract the spatial correlation length ℓ c and extent δ n of subnuclear refractive index fluctuations to provide quantitative information on chromatin distribution. Approach: Since an analytical formulation that links azimuth-resolved signals to ℓ c and δ n is not feasible, we set out to assess the potential of machine learning to predict these parameters via a data-driven approach. We carry out a convolutional neural network (CNN)-based regression analysis on 198 numerically computed signals for nuclear models constructed with ℓ c varying in steps of 0.1 µ m between 0.4 and 1.0 µ m , and δ n varying in steps of 0.005 between 0.005 and 0.035. We quantify the performance of our analysis using a five-fold cross-validation technique. Results: The results show agreement between the true and predicted values for both ℓ c and δ n , with mean absolute percent errors of 8.5% and 13.5%, respectively. These errors are smaller than the minimum percent increment between successive values for respective parameters characterizing the constructed models and thus signify an extremely good prediction performance over the range of interest. Conclusions: Our results reveal that CNN-based regression can be a powerful approach for exploiting the information content of two-dimensional optical scattering signals and hence monitoring chromatin organization in a quantitative manner.

Long-Term Intracellular Tracking of Label-Free Nanoparticles in Live Cells and Tissues with Confocal Microscopy.

The label-free imaging of inorganic nanoparticles (NPs) using confocal laser scanning microscopy (CLSM) provides a powerful and versatile tool for studying interactions between NPs and biological systems. Without the need for exogenous labels or markers, it simply benefits from the differential scattering of visible photons between biomaterials and inorganic NPs. Validation experiments conducted on fixed and living cells in real-time, as well as mouse tissue sections following parenteral administration of NPs. Additionally, by incorporating reporter fluorophores and utilizing both reflectance and fluorescence imaging modalities, the method enables high-resolution multiplex imaging of cellular structures and NPs. Different sizes and concentrations of Au NPs are tested as for Ag, Fe3O4, and CeO2 NPs, all with biological interest. Overall, the comprehensive study of NP imaging by confocal microscopy in reflectance mode provides valuable insights and tools for researchers interested in monitoring the nano-bio interactions.

Numerical analysis and experimental verification of optical scattering from microplastics.

Accurate and fast characterization of the micron-sized plastic particles in aqueous media requires an in-depth understanding of light interaction with these particles. Due to the complexity of Mie scattering theory, the features of the scattered light have rarely been related to the physical properties of these tiny objects. To address this problem, we reveal the relation of the wavelength-dependent optical scattering patterns with the size and refractive index of the particles by numerically studying the angular scattering features. We subsequently present a low-cost setup to measure the optical scattering of the particles. Theoretical investigation shows that the angular distribution of the scattered light by microplastics carries distinct signatures of the particle size and the refractive index. The results can be used to develop a portable, low-cost setup to detect microplastics in water.

Feasibility of postprandial optical scattering of lipoproteins in blood as an optical marker of cardiovascular disease risk: modeling and experimental validation.

Significance: Blood lipid levels (i.e., triglycerides (TGs) and cholesterol) are a strong predictor of cardiovascular disease (CVD) risk. Current methods for measuring blood lipids require invasive blood draws and traditional lab testing, limiting their practicality for frequent monitoring. Optical measurements of lipoproteins, which carry TG and cholesterol in blood, may lead to simpler invasive or non-invasive methods for more frequent and rapid blood lipid measurements. Aim: To investigate the effect of lipoproteins on optical properties of blood before and after a high-fat meal (i.e., the pre- and post-prandial state). Approach: Simulations were performed using Mie theory to estimate lipoprotein scattering properties. A literature review was conducted to identify key simulation parameters including lipoprotein size distributions and number density. Experimental validation of ex-vivo blood samples was conducted using spatial frequency domain imaging. Results: Our results indicated that lipoproteins in blood, particularly very low-density lipoproteins and chylomicrons, are highly scattering in the visible and near-infrared wavelength region. Estimates of the increase in the reduced scattering coefficient (µs') of blood at 730 nm after a high-fat meal ranged from 4% for a healthy individual, to 15% for those with type 2 diabetes, to up to 64% for those suffering from hypertriglyceridemia. A reduction in blood scattering anisotropy (g) also occurred as a function of TG concentration increase. Conclusion: These findings lay the foundation for future research in the development of optical methods for invasive and non-invasive optical measure of blood lipoproteins, which could improve early detection and management of CVD risk.

Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range.

Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 µm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method's power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.

Investigation of silk as a phantom material for ultrasound and photoacoustic imaging.

Comprehensive characterization of biomedical imaging systems require phantoms that are easy to fabricate and can mimic human tissue. Additionally, with the arrival of engineered tissues, it is key to develop phantoms that can mimic bioengineered samples. In ultrasound and photoacoustic imaging, water-soluble phantom materials such as gelatin undergo rapid degradation while polymer-based materials such as polyvinyl alcohol are not conducive for generating bioengineered tissues that can incorporate cells. Here we propose silk protein-based hydrogels as an ultrasound and photoacoustic phantom material that has potential to provide a 3D environment for long-term sustainable cell growth. Common acoustic, optical, and biomechanical properties such as ultrasound attenuation, reduced scattering coefficient, and Young's modulus were measured. The results indicate that silk acoustically mimics many tissue types while exhibiting similar reduced optical scattering in the wavelength range of 400-1200 nm. Furthermore, silk-based materials can be stored long-term with no change in acoustic and optical properties, and hence can be utilized to assess the performance of ultrasound and photoacoustic systems.