Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization

Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection

Near-infrared (NIR) hyperspectral imaging is a powerful technique that enables the capture of three-dimensional (3D) spectra-spatial information within the NIR spectral range, offering a wide array of applications. However, the high cost associated with InGaAs focal plane array (FPA) has impeded the widespread adoption of NIR hyperspectral imaging. Addressing this challenge, in this study, we adopt an alternative approach-single-pixel detection for NIR hyperspectral imaging. Our investigation reveals that single-pixel detection outperforms conventional FPA, delivering a superior signal-to-noise ratio (SNR) for both spectral and imaging reconstruction. To implement this strategy, we leverage self-assembled colloidal quantum dots (CQDs) and a digital micromirror device (DMD) for NIR spectral and spatial information multiplexing, complemented by single-pixel detection for simultaneous spectral and image reconstruction. Our experimental results demonstrate successful NIR hyperspectral imaging with a detection window about 600 nm and an average spectral resolution of 8.6 nm with a pixel resolution of 128 × 128. The resulting spectral and spatial data align well with reference instruments, which validates the effectiveness of our approach. By circumventing the need for expensive and bulky FPA and wavelength selection components, our solution shows promise in advancing affordable and accessible NIR hyperspectral imaging technologies, thereby expanding the range of potential applications.

Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy

The diversity of fungal strains is influenced by genetic and environmental factors, growth conditions and mycelium age, and the spectral features of fungal mycelia are associated with their biochemical, physiological, and structural traits. This study investigates whether intraspecific differences can be detected in two closely related entomopathogenic species, namely Cordyceps farinosa and Cordyceps fumosorosea, using ultraviolet A to shortwave infrared (UVA-SWIR) reflectance spectra. Phylogenetic analysis of all strains revealed a high degree of uniformity among the populations of both species. The characteristics resulting from variation in the species, as well as those resulting from the age of the cultures were determined. We cultured fungi on PDA medium and measured the reflectance of mycelia in the 350-2500 nm range after 10 and 17 days. We subjected the measurements to quadratic discriminant analysis (QDA) to identify the minimum number of bands containing meaningful information. We found that when the age of the fungal culture was known, species represented by a group of different strains could be distinguished with no more than 3-4 wavelengths, compared to 7-8 wavelengths when the age of the culture was unknown. At least 6-8 bands were required to distinguish cultures of a known species among different age groups. Distinguishing all strains within a species was more demanding: at least 10 bands were required for C. fumosorosea and 21 bands for C. farinosa. In conclusion, fungal differentiation using point reflectance spectroscopy gives reliable results when intraspecific and age variations are taken into account.

Dynamic diffusive interfacial transport (D-DIT): A novel quantitative swelling technique for developing binary phase diagrams of aqueous surfactant systems

Current methods to develop surfactant phase diagrams are time-intensive and fail to capture the kinetics of phase evolution. Here, the design and performance of a quantitative swelling technique to study the dynamic phase behavior of surfactants are described. The instrument combines cross-polarized optical and short-wave infrared imaging to enable high-resolution, high-throughput, and in situ identification of phases and water compositions. Data across the entire composition spectrum for the dynamics and phase evolution of a binary aqueous non-ionic surfactant solution at two isotherms are presented. This instrument provides pathways to develop non-equilibrium phase diagrams of surfactant systems-critical to predicting the outcomes of formulation and processing. It can be applied to study time-dependent material relationships across a diverse range of materials and processes, including the dissolution of surfactant droplets and the drying of aqueous polymer films.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN

Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp's reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.

Temperature-Dependent Modulation of Short-Wave-Infrared Light Transparency Based on Associated Structures of a Liquescent Nickel(III) Complex

A liquescent bis(malononitriledithiolato)nickel(III) complex with a bis(methoxyethyl)imidazolium cation, 1[Ni(mnt)2 ], exhibits three-stage thermochromic modulation of transparency/absorption in the short-wave-infrared (SWIR) region (1000-2500 nm), driven by associated structural changes. Upon heating, the electronic spectra of 1[Ni(mnt)2 ] in the SWIR region shift to shorter wavelengths accompanying with the solid-liquid phase transition at 76 °C. Further heating to over 109 °C induces a second transition of the electronic spectra, characterized by a blue-shift of the SWIR absorption in the liquid phase. The results of temperature-dependent electronic spectra and magnetic susceptibility indicated that the thermochromic changes can be attributed to the two-step dissociation of the associated structures of [Ni(mnt)2 ]- , occurring during the solid-liquid phase transition and the shift of dimer-monomer equilibrium in the liquid state. These changes can be visualized using an SWIR imaging camera under appropriate SWIR lights.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

An observation of short-wave near-infrared hyperspectral imaging in tracking of invisible post-traumatic subcutaneous lesions

Post-traumatic soft tissue damage could persist for an extended period, and the non-traumatic side could be affected by indirect consequences. Hyperspectral imaging soft abundance scorer can identify these concealed and asymptomatic lesions.

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery

Significance

Wavelength selection from a large diffuse reflectance spectroscopy (DRS) dataset enables removal of spectral multicollinearity and thus leads to improved understanding of the feature domain. Feature selection (FS) frameworks are essential to discover the optimal wavelengths for tissue differentiation in DRS-based measurements, which can facilitate the development of compact multispectral optical systems with suitable illumination wavelengths for clinical translation.

Aim

The aim was to develop an FS methodology to determine wavelengths with optimal discriminative power for orthopedic applications, while providing the frameworks for adaptation to other clinical scenarios.

Approach

An ensemble framework for FS was developed, validated, and compared with frameworks incorporating conventional algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), and backward interval partial least squares (biPLS).

Results

Via the one-versus-rest binary classification approach, a feature subset of 10 wavelengths was selected from each framework yielding comparable balanced accuracy scores (PCA: 94.8 ± 3.47 % , LDA: 98.2 ± 2.02 % , biPLS: 95.8 ± 3.04 % , and ensemble: 95.8 ± 3.16 % ) to those of using all features (100%) for cortical bone versus the rest class labels. One hundred percent balanced accuracy scores were generated for bone cement versus the rest. Different feature subsets achieving similar outcomes could be identified due to spectral multicollinearity.

Conclusions

Wavelength selection frameworks provide a means to explore domain knowledge and discover important contributors to classification in spectroscopy. The ensemble framework generated a model with improved interpretability and preserved physical interpretation, which serves as the basis to determine illumination wavelengths in optical instrumentation design.

Millimeter-deep micron-resolution vibrational imaging by shortwave infrared photothermal microscopy

Deep-tissue chemical imaging plays a vital role in biological and medical applications. Here, we present a shortwave infrared photothermal (SWIP) microscope for millimeter-deep vibrational imaging with sub-micron lateral resolution and nanoparticle detection sensitivity. By pumping the overtone transition of carbon-hydrogen bonds and probing the subsequent photothermal lens with shortwave infrared light, SWIP can obtain chemical contrast from polymer particles located millimeter-deep in a highly scattering phantom. By fast digitization of the optically probed signal, the amplitude of the photothermal signal is shown to be 63 times larger than that of the photoacoustic signal, thus enabling highly sensitive detection of nanoscale objects. SWIP can resolve the intracellular lipids across an intact tumor spheroid and the layered structure in millimeter-thick liver, skin, brain, and breast tissues. Together, SWIP microscopy fills a gap in vibrational imaging with sub-cellular resolution and millimeter-level penetration, which heralds broad potential for life science and clinical applications.

Optical absorption spectra and corresponding in vivo photoacoustic visualization of exposed peripheral nerves

Significance

Multispectral photoacoustic imaging has the potential to identify lipid-rich, myelinated nerve tissue in an interventional or surgical setting (e.g., to guide intraoperative decisions when exposing a nerve during reconstructive surgery by limiting operations to nerves needing repair, with no impact to healthy or regenerating nerves). Lipids have two optical absorption peaks within the NIR-II and NIR-III windows (i.e., 1000 to 1350 nm and 1550 to 1870 nm wavelength ranges, respectively) which can be exploited to obtain photoacoustic images. However, nerve visualization within the NIR-III window is more desirable due to higher lipid absorption peaks and a corresponding valley in the optical absorption of water.

Aim

We present the first known optical absorption characterizations, photoacoustic spectral demonstrations, and histological validations to support in vivo photoacoustic nerve imaging in the NIR-III window.

Approach

Four in vivo swine peripheral nerves were excised, and the optical absorption spectra of these fresh ex vivo nerves were characterized at wavelengths spanning 800 to 1880 nm, to provide the first known nerve optical absorbance spectra and to enable photoacoustic amplitude spectra characterization with the most optimal wavelength range. Prior to excision, the latter two of the four nerves were surrounded by aqueous, lipid-free, agarose blocks (i.e., 3% w/v agarose) to enhance acoustic coupling during in vivo multispectral photoacoustic imaging using the optimal NIR-III wavelengths (i.e., 1630 to 1850 nm) identified in the ex vivo studies.

Results

There was a verified characteristic lipid absorption peak at 1725 nm for each ex vivo nerve. Results additionally suggest that the 1630 to 1850 nm wavelength range can successfully visualize and differentiate lipid-rich nerves from surrounding water-containing and lipid-deficient tissues and materials.

Conclusions

Photoacoustic imaging using the optimal wavelengths identified and demonstrated for nerves holds promise for detection of myelination in exposed and isolated nerve tissue during a nerve repair surgery, with possible future implications for other surgeries and other optics-based technologies.

Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

SIGNIFICANCE

Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics.

AIM

Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization.

APPROACH

A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( n = 6 ) after the injection of a NB-specific NIR-I fluorescent probe (Dinutuximab-IRDye800). We constructed image cubes representing fluorescence collected from ∼ 850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k -nearest neighbor classification, and a neural network.

RESULTS

The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k -nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively).

CONCLUSIONS

The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.

LED-pumped Er:Cr:YSGG light sources

For, what we believe is, the first time, an Er:Cr:YSGG crystal is pumped by LEDs through a Ce:YAG luminescent concentrator. We demonstrate both laser emission at 2.79 µm and strong spontaneous emission at 1.6 µm. The luminescent concentrator delivers 1.5 ms pulses at 10 Hz in the visible (550-650 nm) to the Er:Cr:YSGG crystal, in a transverse pumping configuration. The Er:Cr:YSGG laser produces up to 6.8 mJ at 2.79 µm in a biconcave cavity. The Er:Cr:YSGG also stands out as a bright broadband incoherent source around 1.6 µm with a unique combination of peak power (351 mW) and brightness (1.4 W/sr/cm2).

Near-Infrared Imaging of Indocyanine Green Identifies Novel Routes of Lymphatic Drainage from Metacarpophalangeal Joints in Healthy Human Hands

Background: Collecting lymphatic vessel (CLV) dysfunction has been implicated in various diseases, including rheumatoid arthritis (RA). RA patients with active hand arthritis exhibit significantly reduced lymphatic clearance of the web spaces adjacent to the metacarpophalangeal (MCP) joints and a reduction in total and basilic-associated CLVs on the dorsal surface of the hand by near-infrared (NIR) imaging of indocyanine green (ICG). In this pilot study, we assessed direct lymphatic drainage from MCP joints and aimed to visualize the total lymphatic anatomy using novel dual-agent relaxation contrast magnetic resonance lymphography (DARC-MRL) in the upper extremity of healthy human subjects. Methods and Results: Two healthy male subjects >18 years old participated in the study. We performed NIR imaging along with conventional- or DARC-MRL following intradermal web space and intra-articular MCP joint injections. ICG (NIR) or gadolinium (Gd) (MRL) was administered to visualize the CLV anatomy of the upper extremity. Web space draining CLVs were associated with the cephalic side of the antecubital fossa, while MCP draining CLVs were localized to the basilic side of the forearm by near-infrared indocyanine green imaging. The DARC-MRL methods used in this study did not adequately nullify the contrast in the blood vessels, and limited Gd-filled CLVs were identified. Conclusion: MCP joints predominantly drain into basilic CLVs in the forearm, which may explain the reduction in basilic-associated CLVs in the hands of RA patients. Current DARC-MRL techniques show limited identification of healthy lymphatic structures, and further refinement in this technique is necessary. Clinical trial registration number: NCT04046146.

Rejuvenating old fluorophores with new chemistry

The field of organic chemistry began with 19th century scientists identifying and then expanding upon synthetic dye molecules for textiles. In the 20th century, dye chemistry continued with the aim of developing photographic sensitizers and laser dyes. Now, in the 21st century, the rapid evolution of biological imaging techniques provides a new driving force for dye chemistry. Of the extant collection of synthetic fluorescent dyes for biological imaging, two classes reign supreme: rhodamines and cyanines. Here, we provide an overview of recent examples where modern chemistry is used to build these old-but-venerable classes of optically responsive molecules. These new synthetic methods access new fluorophores, which then enable sophisticated imaging experiments leading to new biological insights.

Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra

The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the experimental spectra collected with a medical device, and spectra generated by the photons transport numerical simulations. This comparison is usually limited to spectral shape due to the absence of intensity standardization of the experimental DR spectra. This study proposes to theoretically (using photometric calculation) and experimentally (from experimental spectra acquired on optical phantom) establish a corrective factor to obtain common intensity unit for experimental and simulated signals.

Relevance and utility of the in-vivo and ex-vivo optical properties of the skin reported in the literature: a review [Invited]

Imaging non-invasively into the human body is currently limited by cost (MRI and CT scan), image resolution (ultrasound), exposure to ionising radiation (CT scan and X-ray), and the requirement for exogenous contrast agents (CT scan and PET scan). Optical imaging has the potential to overcome all these issues but is currently limited by imaging depth due to the scattering and absorption properties of human tissue. Skin is the first barrier encountered by light when imaging non-invasively, and therefore a clear understanding of the way that light interacts with skin is required for progress on optical medical imaging to be made. Here we present a thorough review of the optical properties of human skin measured in-vivo and compare these to the previously collated ex-vivo measurements. Both in-vivo and ex-vivo published data show high inter- and intra-publication variability making definitive answers regarding optical properties at given wavelengths challenging. Overall, variability is highest for ex-vivo absorption measurements with differences of up to 77-fold compared with 9.6-fold for the in-vivo absorption case. The impact of this variation on optical penetration depth and transport mean free path is presented and potential causes of these inconsistencies are discussed. We propose a set of experimental controls and reporting requirements for future measurements. We conclude that a robust in-vivo dataset, measured across a broad spectrum of wavelengths, is required for the development of future technologies that significantly increase the depth of optical imaging.

Label-Free Optical Metabolic Imaging in Cells and Tissues

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed.

Noninvasive Monitoring to Detect Dehydration: Are We There Yet?

The need for hydration monitoring is significant, especially for the very young and elderly populations who are more vulnerable to becoming dehydrated and suffering from the effects that dehydration brings. This need has been among the drivers of considerable effort in the academic and commercial sectors to provide a means for monitoring hydration status, with a special interest in doing so outside the hospital or clinical setting. This review of emerging technologies provides an overview of many technology approaches that, on a theoretical basis, have sensitivity to water and are feasible as a routine measurement. We review the evidence of technical validation and of their use in humans. Finally, we highlight the essential need for these technologies to be rigorously evaluated for their diagnostic potential, as a necessary step to meet the need for hydration monitoring outside of the clinical environment.

Measurement of rat and human tissue optical properties for improving the optical detection and visualization of peripheral nerves

Peripheral nerve damage frequently occurs in challenging surgical cases resulting in high costs and morbidity. Various optical techniques have proven effective in detecting and visually enhancing nerves, demonstrating their translational potential for assisting in nerve-sparing medical procedures. However, there is limited data characterizing the optical properties of nerves in comparison to surrounding tissues, thus limiting the optimization of optical nerve detection systems. To address this gap, the absorption and scattering properties of rat and human nerve, muscle, fat, and tendon were determined from 352-2500 nm. The optical properties highlighted an ideal region in the shortwave infrared for detecting embedded nerves, which remains a significant challenge for optical approaches. A 1000-1700 nm hyperspectral diffuse reflectance imaging system was used to confirm these results and identify optimal wavelengths for nerve imaging contrast in an in vivo rat model. Optimal nerve visualization contrast was achieved using 1190/1100 nm ratiometric imaging and was sustained for nerves embedded under ≥600 µm of fat and muscle. Overall, the results provide valuable insights for optimizing the optical contrast of nerves, including those embedded in tissue, which could lead to improved surgical guidance and nerve-sparing outcomes.

Azimuth mapping of fibrous tissue in linear dichroism-sensitive photoacoustic microscopy

Photoacoustic imaging (PAI) has emerged as a molecular-selective imaging technology based on optical absorption contrast. Dichroism-sensitive photoacoustic (DS-PA) imaging has been reported, where the absorption coefficient has a vector characteristic, featuring dimensions of contrast in polarization and wavelength. Herein, we present a DS-PA microscopy (DS-PAM) system that implements optical anisotropy contrast and molecular selectivity. Moreover, we propose mathematical solutions to fully derive dichroic properties. A wavelength for the PAI of collagenous tissue was used, and the proposed algorithms were validated using linear dichroic materials. We successfully mapped dichroic information in fibrous tissue imaging based on the degree of anisotropy and axis orientation, and also deduced mechanical assessment from the tissue arrangement. The proposed DS-PAM system and algorithms have great potential in various diagnostic fields using polarimetry, such as musculoskeletal and cardiovascular systems.

1.8-µm laser operation based on femtosecond-laser direct written Tm:YVO4 cladding waveguides

In this work, we have demonstrated tunable 1.8-µm laser operation based on a Tm:YVO₄ cladding waveguide fabricated by means of femtosecond laser direct writing. Benefiting from the good optical confinement of the fabricated waveguide, efficient thulium laser operation, with a maximum slope efficiency of 36%, a minimum lasing threshold of 176.8 mW, and a tunable output wavelength from 1804 to 1830nm, has been achieved in a compact package via adjusting and optimizing the pump and resonant conditions of the waveguide laser design. The lasing performance using output couplers with different reflectivity has been well studied in detail. In particular, due to the good optical confinement and relatively high optical gain of the waveguide design, efficient lasing can be obtained even without using any cavity mirrors, thereby opening up new possibilities for compact and integrated mid-infrared laser sources.

Development of a compounded propofol nanoemulsion using multiple non-invasive process analytical technologies

Propofol is the preferred anaesthetic for induction and maintenance of sedation in critically ill mechanically ventilated COVID-19 patients. However, during the outbreak of the COVID-19 pandemic, regular supply chains could not keep up with the sudden increase in global demand, causing drug shortages. Propofol is formulated as an oil-in-water emulsion which is administered intravenously. This study explores the extemporaneous preparation of a propofol emulsion without specialized manufacturing equipment to temporally alleviate such shortages. A commercially available lipid emulsion (IVLE, SMOFlipid 20%), intended for parenteral nutrition, was used to create a propofol loaded nanoemulsion via addition of liquid propofol drug substance and subsequent mixing. Critical quality attributes such as mean droplet size and the volume-weighted percentage of large-diameter (>5µm) droplets were studied. The evolution of droplet size and propofol distribution was monitored in situ and non-destructively, maintaining sterility, using Spatially Resolved Dynamic Light Scattering and Near Infrared Spectroscopy, respectively. Using response surface methodology, an optimum was found for a 4% w/v propofol formulation with a ∼15 minute mixing time in a flask shaker at a 40° shaking angle. This study shows that extemporaneous compounding is a viable option for emergency supply of propofol drug product during global drug shortages.

Hyperspectral imaging of lipids in biological tissues using near-infrared and shortwave infrared transmission mode: A pilot study

Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the near-infrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter. The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins, and water. The enhanced protocol demonstrates the ability to visualize small adipose inclusions of 1-10 μm size.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a R_cv² in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

Face presentation attack detection: Research opportunities and perspectives

The rapid development of biometric methods and their implementation in practice has led to the widespread attacks called spoofing, which are purely biometric vulnerabilities, but are not used in conjunction with other IT security solutions. Although biometric recognition as a branch of computer science dates back to the 1960s, attacks on biometric systems have become more sophisticated since the 2010s due to great advances in pattern recognition. It should be noted that face recognition is the most attractive topic for deceiving recognition systems. Popular presentation attacks, such as print, replay and mask attacks, have demonstrated a high security risk for SOTA face recognition systems. Many Presentation Attack Detection (PAD) methods (also known as face anti-spoofing methods or countermeasures) have been proposed that can automatically detect and mitigate such targeted attacks. The article presents a systematic survey in face anti-spoofing with prognostic trends in this research area. A brief description of 16 outstanding previous surveys on the face PAD field is mentioned, from which it is possible to trace how this scientific topic has developed. SOTA in PAD provides an analysis of a wide range of the PAD methods, which are categorized into two unbalanced groups: digital (feature-based) and physical (sensor-based) methods. Generalization of deep learning methods as a recent trend aimed at improving recognition results requires special attention. This survey presents five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods. A summary of over than 40 existing 2D/3D face spoofing databases is a guideline for those who want to select databases for experiments. One can also find a description of performance evaluation metrics and testing protocols. In addition, we discuss trends and perspectives in the emerging field of facial biometrics.

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R² in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

A new label-free optical imaging method for the lymphatic system enhanced by deep learning

Our understanding of the lymphatic vascular system lags far behind that of the blood vascular system, limited by available imaging technologies. We present a label-free optical imaging method that visualizes the lymphatic system with high contrast. We developed an orthogonal polarization imaging (OPI) in the shortwave infrared range (SWIR) and imaged both lymph nodes and lymphatic vessels of mice and rats in vivo through intact skin, as well as human mesenteric lymph nodes in colectomy specimens. By integrating SWIR-OPI with U-Net, a deep learning image segmentation algorithm, we automated the lymph node size measurement process. Changes in lymph nodes in response to cancer progression were monitored in two separate mouse cancer models, through which we obtained insights into pre-metastatic niches and correlation between lymph node masses and many important biomarkers. In a human pilot study, we demonstrated the effectiveness of SWIR-OPI to detect human lymph nodes in real time with clinical colectomy specimens.

One Sentence Summary

We develop a real-time high contrast optical technique for imaging the lymphatic system, and apply it to anatomical pathology gross examination in a clinical setting, as well as real-time monitoring of tumor microenvironment in animal studies.

Spatiotemporal Control of Biology: Synthetic Photochemistry Toolbox with Far-Red and Near-Infrared Light

The complex network of naturally occurring biological pathways motivates the development of new synthetic molecules to perturb and/or detect these processes for fundamental research and clinical applications. In this context, photochemical tools have emerged as an approach to control the activity of drug or probe molecules at high temporal and spatial resolutions. Traditional photochemical tools, particularly photolabile protecting groups (photocages) and photoswitches, rely on high-energy UV light that is only applicable to cells or transparent model animals. More recently, such designs have evolved into the visible and near-infrared regions with deeper tissue penetration, enabling photocontrol to study biology in tissue and model animal contexts. This Review highlights recent developments in synthetic far-red and near-infrared photocages and photoswitches and their current and potential applications at the interface of chemistry and biology.

Detection of DLBCL by pixel purity index and iterative linearly constrained minimum variance into hyperspectral imaging analysis

It is unclear whether a hyperspectral imaging-based approach can facilitate the diagnosis of diffuse large B-cell lymphoma (DLBCL), and further investigation is required. In this study, the pixel purity index (PPI) coupled with iterative linearly constrained minimum variance (ILCMV) was used to bridge this gap. We retrospectively reviewed 22 pathological DLBCL specimens. Ten normal lymph node specimens were used as controls. PPI endmember extraction was performed to identify seed-training samples. ILCMV was then used to classify cell regions. The 3D receiver operating characteristic (ROC) showed that the spectral information divergence possessed superior ability to distinguish between normal and abnormal lymphoid cells owing to its stronger background suppression compared with the spectral angle mapper and mean square error methods. An automated cell hyperspectral image classification approach that combined the PPI and ILCMV was used to improve DLBCL diagnosis. This strategy intelligently resolved critical problems arising in unsupervised classification.

Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra

The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues and to determine whether an extended wavelength range (450-1550 nm) offers any advantages over using the conventional wavelength range. Furthermore, multivariate analysis combined with a machine learning algorithm, either linear discriminant analysis or the more advanced support vector machine, was used to discriminate between and classify freshly excised human liver specimens from 18 patients. Tumors were distinguished from surrounding liver tissues with a sensitivity of 99%, specificity of 100%, classification rate of 100% and a Matthews correlation coefficient of 100% using the extended wavelength range and a combination of principal component analysis and support vector techniques. The results indicate that this technology may be useful in clinical applications for real-time tissue diagnostics of tumor margins where rapid classification is important.

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

Model-based characterization platform of fiber optic extended-wavelength diffuse reflectance spectroscopy for identification of neurovascular bundles

SIGNIFICANCE

Fiber-optic extended-wavelength diffuse reflectance spectroscopy (EWDRS) using both visible/near-infrared and shortwave-infrared detectors enables improved detection of spectral absorbances arising from lipids, water, and collagen and has demonstrated promise in a variety of applications, including detection of nerves and neurovascular bundles (NVB). Development of future applications of EWDRS for nerve detection could benefit from the use of model-based analyses including Monte Carlo (MC) simulations and evaluation of agreement between model systems and empirical measurements.

AIM

The aim of this work is to characterize agreement between EWDRS measurements and simulations and inform future applications of model-based studies of nerve-detecting applications.

APPROACH

A model-based platform consisting of an ex vivo microsurgical nerve dissection model, unique two-layer optical phantoms, and MC model simulations of fiber-optic EWDRS spectroscopic measurements were used to characterize EWDRS and compare agreement across models. In addition, MC simulations of an EWDRS measurement scenario are performed to provide a representative example of future analyses.

RESULTS

EWDRS studies performed in the common chicken thigh femoral nerve microsurgical dissection model indicate similar spectral features for classification of NVB versus adjacent tissues as reported in porcine models and human subjects. A comparison of measurements from unique EWDRS issue mimicking optical phantoms and MC simulations indicates high agreement between the two in homogeneous and two-layer optical phantoms, as well as in dissected tissues. Finally, MC simulations of measurement over a simulated NVB indicate the potential of future applications for measurement of nerve plexus.

CONCLUSIONS

Characterization of agreement between fiber-optic EWDRS measurements and MC simulations demonstrates strong agreement across a variety of tissues and optical phantoms, offering promise for further use to guide the continued development of EWDRS for translational applications.

Heterojunction bilayers serving as a charge transporting interlayer reduce the dark current and enhance photomultiplication in organic shortwave infrared photodetectors

Previous approaches to induce photomultiplication in organic diodes have increased the photosignal but lacked control over reducing background noise. This work presents a new interlayer design based on a heterojunction bilayer that concurrently enables photomultiplication and suppresses the dark current in organic shortwave infrared detectors to improve the overall detectivity. The heterojunction bilayer consists of a hole-transporting material copper thiocyanate and an electron-transporting material tin oxide, and this combination offers the ability to block charge injection in the dark. Under illumination, the bilayer promotes trap-assisted photomultiplication by lowering the tunneling barrier and amplifying the photocurrent through the injection of multiple carriers per absorbed photon. Upon incorporating the heterojunction interlayer in photodiodes and upconversion imagers, the devices achieve an external quantum efficiency up to 560% and a detectivity of 3.5 × 10⁹ Jones. The upconversion efficiency of the imager doubles with a 1.7 fold improvement in contrast compared to the imager without the heterojunction interlayer. The new interlayer design is generalizable to work with different organic semiconductors, making it attractive and easy to integrate with emerging organic infrared systems.

Temporal and spatial summation of laser heat stimuli in cultured nociceptive neurons of the rat

We studied the efficacy of a near-infrared laser (1475 nm) to activate rat dorsal root ganglion (DRG) neurons with short punctate radiant heat pulses (55 µm diameter) and investigated temporal and spatial summation properties for the transduction process for noxious heat at a subcellular level. Strength-duration curves (10–80 ms range) indicated a minimum power of 30.2mW for the induction of laser-induced calcium transients and a chronaxia of 13.9 ms. However, threshold energy increased with increasing stimulus duration suggesting substantial radial cooling of the laser spot. Increasing stimulus duration demonstrated suprathreshold intensity coding of calcium transients with less than linear gains (Stevens exponents 0.29/35mW, 0.38/60mW, 0.46/70mW). The competitive TRPV1 antagonist capsazepine blocked responses to short near-threshold stimuli and significantly reduced responses to longer duration suprathreshold heat. Heating 1/3 of the soma of a neuron was sufficient to induce calcium transients significantly above baseline (p < 0.05), but maximum amplitude was only achieved by centering the laser over the entire neuron. Heat-induced calcium increase was highest in heated cell parts but rapidly reached unstimulated areas reminiscent of spreading depolarization and opening of voltage-gated calcium channels. Full intracellular equilibrium took about 3 s, consistent with a diffusion process. In summary, we investigated transduction mechanisms for noxious laser heat pulses in native sensory neurons at milliseconds temporal and subcellular spatial resolution and characterized strength duration properties, intensity coding, and spatial summation within single neurons. Thermal excitation of parts of a nociceptor spread via both membrane depolarization and intracellular calcium diffusion.

Management of fluorescent organic/inorganic nanohybrids for biomedical applications in the NIR-II region

Biomedical fluorescence imaging in the second near-infrared (NIR-II, 100-1700 nm) window provides great potential for visualizing physiological and pathological processes, owing to the reduced tissue absorption, scattering, and autofluorescence. Various types of NIR-II probes have been reported in the past decade. Among them, NIR-II organic/inorganic nanohybrids have attracted widespread attention due to their unique properties by integrating the advantages of both organic and inorganic species. Versatile organic/inorganic nanohybrids provide the possibility of realizing a combination of functions, controllable size, and multiple optical features. This tutorial review summarizes the reported organic and inorganic species in nanohybrids, and their biomedical applications in NIR-II fluorescence and lifetime imaging. Finally, the challenges and outlook of organic/inorganic nanohybrids in biomedical applications are discussed.

Ultra-fast detail enhancement for a short-wave infrared image

Image detail enhancement is critical to the performance of short-wave infrared (SWIR) imaging systems, especially to the long-range systems. However, the existing high-performance infrared (IR) image enhancement methods typically have difficulty in meeting the requirements of the imaging system with high resolution and high frame rate. In this paper, we propose an ultra-fast and simple SWIR image detail enhancement method based on the difference of Gaussian (DoG) filter and plateau equalization. Our method consists of efficient edge detail information extraction and histogram equalization. The experimental results demonstrated that the proposed method achieves outstanding enhancement performance with a frame rate around 50 fps for 1280×1024 SWIR images.

Shortwave infrared spatial frequency domain imaging for non-invasive measurement of tissue and blood optical properties

SIGNIFICANCE

The shortwave infrared (SWIR) optical window (∼900 to 2000 nm) has attracted interest for deep tissue imaging due to the lower scattering of light. SWIR spatial frequency domain imaging (SWIR SFDI) provides wide-field tissue optical property measurements in this wavelength band. Key design and performance characteristics, such as portability, wavelength selection, measurement resolution, and the effect of skin have not yet been addressed for SWIR SFDI.

AIM

To fabricate and characterize a SWIR SFDI system for clinical use.

APPROACH

The optimal choice of wavelengths was identified based on optical property uncertainty estimates and imaging depth. A compact light-emitting diode-based dual wavelength SWIR SFDI system was fabricated. A two-layer inverse model was developed to account for the layered structure of skin. Performance was validated using tissue-simulating phantoms and in-vivo measurements from three healthy subjects.

RESULTS

The SWIR SFDI system had a μs' resolution of at least 0.03  mm  -  1 at 880 nm and 0.02  mm  -  1 at 1100 nm. The two-layer inverse model reduced the error in deeper layer μs' extractions by at least 24% in the phantom study. The two-layer model also increased the contrast between superficial vessels and the surrounding tissue for in-vivo measurements.

CONCLUSION

The clinic-ready SWIR SFDI device is sensitive to small optical property alterations in diffuse media, provides enhanced accuracy in quantifying optical properties in the deeper layers in phantoms, and provided enhanced contrast of subcutaneous blood vessels.

Safety of use of the ENDOSWIR near-infrared optical imaging device on human tissues: prospective blind study

Cancer surgery requires removing the tumor tissue in necessary and sufficient quantities. Spectral optical imaging in the short-wave infrared (900-1700 nm) could provide an intraoperative guidance to the surgeon based on the absorption of the tissues without contrast agent. Our objective was to ensure the safety of our ENDOSWIR device on human tissues. Histological analysis of fresh human tonsils exposed to the SWIR light or not was compared and showed no histological differences. This demonstrates the safety of using the SWIR device on human tissues and allows us to initiate a clinical study for the resection of tumors intraoperatively.

Reflectance spectroscopy and machine learning as a tool for the categorization of twin species based on the example of the Diachrysia genus

In our work we used noninvasive point reflectance spectroscopy in the range from 400 to 2100 nm coupled with machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. We used our approach to distinguish between these species of moths. The basis for the study was a statistically significant collection of 95 specimens identified based on morphological feature and gathered during 23 years in Poland. The numerical part of an experiment included two independent discriminant analyses: stochastic and deterministic. The more sensitive stochastic approach achieved average compliance with the species identification made by entomologists at the level of 99-100%. It demonstrated high stability against the different configurations of training and validation sets, hence strong predictors of Diachrysia siblings distinctiveness. Both methods resulted in the same small set of relevant features, where minimal fully discriminating subsets of wavelengths were three for glass scales on the golden area and four for the brown. The differences between species in scales primarily concern their major components and ultrastructure. In melanin-absent glass scales, this is mainly chitin configuration, while in melanin-present brown scales, melanin reveals as an additional factor.

Blue LED-pumped intense short-wave infrared luminescence based on Cr3+-Yb3+-co-doped phosphors

The growing demand for spectroscopy applications in the areas of agriculture, retail and healthcare has led to extensive research on infrared light sources. The ability of phosphors to absorb blue light from commercial LED and convert the excitation energy into long-wavelength infrared luminescence is crucial for the design of cost-effective and high-performance phosphor-converted infrared LEDs. However, the lack of ideal blue-pumped short-wave infrared (SWIR) phosphors with an emission peak longer than 900 nm greatly limits the development of SWIR LEDs using light converter technology. Here we have developed a series of SWIR-emitting materials with high luminescence efficiency and excellent thermal stability by co-doping Cr³⁺-Yb³⁺ ion pairs into Lu_0.2Sc_0.8BO₃ host materials. Benefitting from strong light absorption of Cr³⁺ in the blue waveband and very efficient Cr³⁺→Yb³⁺ energy transfer, the as-synthesized Lu_0.2Sc_0.8BO₃:Cr³⁺,Yb³⁺ phosphor emits intense SWIR light in the 900-1200 nm from Yb³⁺ under excitation with blue light at ~460 nm. The optimized phosphor presents an internal quantum yield of 73.6% and the SWIR luminescence intensity at 100 °C can still keep 88.4% of the starting value at 25 °C. SWIR LED prototype device based on Lu_0.2Sc_0.8BO₃:Cr³⁺,Yb³⁺ phosphor exhibits exceptional luminescence performance, delivering SWIR radiant power of 18.4 mW with 9.3% of blue-to-SWIR power conversion efficiency and 5.0% of electricity-to-SWIR light energy conversion efficiency at 120 mA driving current. Moreover, under the illumination of high-power SWIR LED, covert information identification and night vision lighting have been realized, demonstrating a very bright prospect for practical applications.

High Spatial and Temporal Resolution NIR-IIb Gastrointestinal Imaging in Mice

Conventional biomedical imaging modalities, including endoscopy, X-rays, and magnetic resonance, are invasive and insufficient in spatial and temporal resolutions for gastrointestinal (GI) tract imaging to guide prognosis and therapy. Here we report a noninvasive method based on lanthanide-doped nanocrystals with ∼1530 nm fluorescence in the near-infrared-IIb window (NIR-IIb, 1500-1700 nm). The rational design of nanocrystals have led to an absolute quantum yield (QY) up to 48.6%. Further benefiting from the minimized scattering through the NIR-IIb window, we enhanced the spatial resolution to ∼1 mm in GI tract imaging, which is ∼3 times higher compared with the near-infrared-IIa (NIR-IIa, 1000-1500 nm) method. The approach also realized a high temporal resolution of 8 frames per second; thus the moment of mice intestinal peristalsis can be captured. Furthermore, with a light-sheet imaging system, we demonstrated a three-dimensional (3D) imaging on the GI tract. Moreover, we successfully translated these advances to diagnose inflammatory bowel disease.

Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases

Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.