One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 μs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

Ultraflexible Wireless Imager Integrated with Organic Circuits for Broadband Infrared Thermal Analysis

Flexible imagers are currently under intensive development as versatile optical sensor arrays, designed to capture images of surfaces and internals, irrespective of their shape. A significant challenge in developing flexible imagers is extending their detection capabilities to encompass a broad spectrum of infrared light, particularly terahertz (THz) light at room temperature. This advancement is crucial for thermal and biochemical applications. In this study, a flexible infrared imager is designed using uncooled carbon nanotube (CNT) sensors and organic circuits. The CNT sensors, fabricated on ultrathin 2.4 µm substrates, demonstrate enhanced sensitivity across a wide infrared range, spanning from near-infrared to THz wavelengths. Moreover, they retain their characteristics under bending and crumpling. The design incorporates light-shielded organic transistors and circuits, functioning reliably under light irradiation, and amplifies THz detection signals by a factor of 10. The integration of both CNT sensors and shielded organic transistors into an 8 × 8 active-sensor matrix within the imager enables sequential infrared imaging and nondestructive assessment for heat sources and in-liquid chemicals through wireless communication systems. The proposed imager, offering unique functionality, shows promise for applications in biochemical analysis and soft robotics.

Infrared Thermography Monitoring of Durum and Common Wheat for Adaptability Assessing and Yield Performance Prediction

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p < 0.001 and p < 0.05), providing interesting information for their early prediction.

Monitoring SF6 Gas Leakage Based on a Customized Binocular System

Sulfur hexafluoride (SF6) gas is extensively utilized as an insulating and arc-quenching medium in the circuit breakers and isolating switches of electrical equipment. It effectively isolates the circuits from the atmosphere and promptly extinguishes arcs. Therefore, the issue of SF6 gas leakage poses a significant threat to the related application fields, and the detection of SF6 gas leakage becomes extremely important. Infrared imaging detection offers advantages including non-contact, high precision, and visualization. However, most existing infrared detection systems are equipped with only one filter to detect SF6 gas. The images captured contain background noise and system noise, making these systems vulnerable to interference from such noises. To address these issues, we propose a method for monitoring SF6 gas leakage based on a customized binocular imaging (CBI) system. The CBI system has two filters, greatly reducing the interference of system noise and background noise. The first filter features the absorption resonant peak of SF6 gas. The second filter is used to record background noise and system noise. One aspect to note is that, in order to avoid the interference of other gases, the central wavelength of this second filter should keep away from the absorption resonant peaks of those gases. Accordingly, the central wavelengths of our customized filters were determined as 10,630 nm and 8370 nm, respectively. Then, two cameras of the same type were separately assembled with a customized filter, and the CBI prototype was accomplished. Finally, we utilized the difference method using two infrared images captured by the CBI system, to monitor the SF6 gas leakage. The results demonstrate that our developed system achieves a high accuracy of over 99.8% in detecting SF6 gas. Furthermore, the CBI system supports a plug-and-play customization to detect various gases for different scenarios.

Photothermal effect in X-ray images for computed tomography of metallic parts: Stainless steel spheres

BACKGROUND

The environmental impact on industrial X-ray tomography systems has gained its attention in terms of image precision and metrology over recent years, yet is still complex due to the variety of applications.

OBJECTIVE

The current study explores the photothermal repercussions of the overall radiation exposure time. It shows the emerging dimensional uncertainty when measuring a stainless steel sphere by means of circular tomography scans.

METHODS

The authors develop a novel frame difference method for X-ray radiographies to evaluate the spatial changes induced in the projected absorption maps on the X-ray panel. The object of interest has a simple geometry for the purpose of proof of concept. The dominant source of the observed radial uncertainty is the photothermal effect due to high-energy X-ray scattering at the metal workpiece. Thermal variations are monitored by an infrared camera within the industrial tomography system, which confines that heat in the industrial grade X-ray system.

RESULTS

The authors demonstrate that dense industrial computed tomography programs with major X-ray power notably affect the uncertainty of digital dimensional measurements. The registered temperature variations are consistent with dimensional changes in radiographies and hence form a source of error that might result in visible artifacts within the 3D image reconstruction.

CONCLUSIONS

This contribution is of fundamental value to reach the balance between the number of projections and radial uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography.

Black Silicon as Anti-Reflective Structure for Infrared Imaging Applications

For uncooled infrared cameras based on microbolometers, silicon caps are often utilized to maintain a vacuum inside the packaged bolometer array. To reduce Fresnel reflection losses, anti-reflection coatings are typically applied on both sides of the silicon caps.This work investigates whether black silicon may be used as an alternative to conventional anti-reflective coatings. Reactive ion etching was used to etch the black silicon layer and deep cavities in silicon. The effects of the processed surfaces on optical transmission and image quality were investigated in detail by Fourier transform infrared spectroscopy and with modulated transfer function measurements. The results show that the etched surfaces enable similar transmission to the state-of-the-artanti-reflection coatings in the 8-12 µm range and possibly obtain wider bandwidth transmission up to 24 µm. No degradation in image quality was found when using the processed wafers as windows. These results show that black silicon can be used as an effective anti-reflection layer on silicon caps used in the vacuum packaging of microbolometer arrays.

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Vibrational sum-frequency generation (VSFG), a second-order nonlinear optical signal, has traditionally been used to study molecules at interfaces as a spectroscopy technique with a spatial resolution of ~100 µm. However, the spectroscopy is not sensitive to the heterogeneity of a sample. To study mesoscopically heterogeneous samples, we, along with others, pushed the resolution limit of VSFG spectroscopy down to ~1 µm level and constructed the VSFG microscope. This imaging technique not only can resolve sample morphologies through imaging, but also record a broadband VSFG spectrum at every pixel of the images. Being a second-order nonlinear optical technique, its selection rule enables the visualization of non-centrosymmetric or chiral self-assembled structures commonly found in biology, materials science, and bioengineering, among others. In this article, the audience will be guided through an inverted transmission design that allows for imaging unfixed samples. This work also showcases that VSFG microscopy can resolve chemical-specific geometric information of individual self-assembled sheets by combining it with a neural network function solver. Lastly, the images obtained under brightfield, SHG, and VSFG configurations of various samples briefly discuss the unique information revealed by VSFG imaging.

Intermediate Antiparallel β Structure in Amyloid β Plaques Revealed by Infrared Spectroscopic Imaging

Aggregation of amyloid β (Aβ) peptides into extracellular plaques is a hallmark of the molecular pathology of Alzheimer's disease (AD). Amyloid aggregates have been extensively studied in vitro, and it is well-known that mature amyloid fibrils contain an ordered parallel β structure. The structural evolution from unaggregated peptide to fibrils can be mediated through intermediate structures that deviate significantly from mature fibrils, such as antiparallel β-sheets. However, it is currently unknown if these intermediate structures exist in plaques, which limits the translation of findings from in vitro structural characterizations of amyloid aggregates to AD. This arises from the inability to extend common structural biology techniques to ex vivo tissue measurements. Here we report the use of infrared (IR) imaging, wherein we can spatially localize plaques and probe their protein structural distributions with the molecular sensitivity of IR spectroscopy. Analyzing individual plaques in AD tissues, we demonstrate that fibrillar amyloid plaques exhibit antiparallel β-sheet signatures, thus providing a direct connection between in vitro structures and amyloid aggregates in the AD brain. We further validate results with IR imaging of in vitro aggregates and show that the antiparallel β-sheet structure is a distinct structural facet of amyloid fibrils.

Thermal Imaging Detection System: A Case Study for Indoor Environments

Currently, there is an increasing need for reliable mechanisms for automatically detecting and localizing people-from performing a people-flow analysis in museums and controlling smart homes to guarding hazardous areas like railway platforms. A method for detecting people using FLIR Lepton 3.5 thermal cameras and Raspberry Pi 3B+ computers was developed. The method creates a control and capture library for the Lepton 3.5 and a new person-detection technique that uses the state-of-the-art YOLO (You Only Look Once) real-time object detector based on deep neural networks. A thermal unit with an automated configuration using Ansible encapsulated in a custom 3D-printed enclosure was used. The unit has applications in simple thermal detection based on the modeling of complex scenes with polygonal boundaries and multiple thermal camera monitoring. An easily deployable person-detection and -localization system based on thermal imaging that supports multiple cameras and can serve as an input for other systems that take actions by knowing the positions of people in monitored environments was created. The thermal detection system was tested on a people-flow analysis performed in the Czech National Museum in Prague. The contribution of the presented method is the development of a small and simple detection system that is easily mountable with wide indoor as well as outdoor applications. The novelty of the system is in the utilization of the YOLO model for thermal data.

Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach

Infrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complimentary information source for more classical histopathology, which makes it a noteworthy tool given possible clinical application. This study aims to build a robust, pixel level machine learning model for pancreatic cancer detection using IR imaging. In this article, we report a pancreatic cancer classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. To fully research model's classification ability, we measured tissues using two optical setups, resulting in Standard and High Definitions data. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. The first six-class model created for comprehensive histopathology achieved pixel (tissue) level AUC values above 0.95, giving a successful technique for digital staining with biochemical information extracted from IR spectra.

Laser-Activated Second Harmonic Generation in Flexible Membrane with Si Nanowires

Nonlinear silicon photonics has a high compatibility with CMOS technology and therefore is particularly attractive for various purposes and applications. Second harmonic generation (SHG) in silicon nanowires (NWs) is widely studied for its high sensitivity to structural changes, low-cost fabrication, and efficient tunability of photonic properties. In this study, we report a fabrication and SHG study of Si nanowire/siloxane flexible membranes. The proposed highly transparent flexible membranes revealed a strong nonlinear response, which was enhanced via activation by an infrared laser beam. The vertical arrays of several nanometer-thin Si NWs effectively generate the SH signal after being exposed to femtosecond infrared laser irradiation in the spectral range of 800-1020 nm. The stable enhancement of SHG induced by laser exposure can be attributed to the functional modifications of the Si NW surface, which can be used for the development of efficient nonlinear platforms based on silicon. This study delivers a valuable contribution to the advancement of optical devices based on silicon and presents novel design and fabrication methods for infrared converters.

Intermediate antiparallel beta structure in amyloid plaques revealed by infrared spectroscopic imaging

Aggregation of amyloid beta (Aβ) peptides into extracellular plaques is a hallmark of the molecular pathology of Alzheimer's disease (AD). Amyloid aggregates have been extensively studied in-vitro, and it is well known that mature amyloid fibrils contain an ordered parallel β structure. The structural evolution from unaggregated peptide to fibrils can be mediated through intermediate structures that deviate significantly from mature fibrils, such as antiparallel β-sheets. However, it is currently unknown if these intermediate structures exist in plaques, which limits the translation of findings from in-vitro structural characterizations of amyloid aggregates to AD. This arises from the inability to extend common structural biology techniques to ex-vivo tissue measurements. Here we report the use of infrared (IR) imaging, wherein we can spatially localize plaques and probe their protein structural distributions with the molecular sensitivity of IR spectroscopy. Analyzing individual plaques in AD tissues, we demonstrate that fibrillar amyloid plaques exhibit antiparallel β-sheet signatures, thus providing a direct connection between in-vitro structures and amyloid aggregates in AD brain. We further validate results with IR imaging of in-vitro aggregates and show that antiparallel β-sheet structure is a distinct structural facet of amyloid fibrils.

Infrared Imaging Analysis of Green Composite Materials during Inline Quasi-Static Flexural Test: Monitoring by Passive and Active Approaches

Composite materials have been used for many years in a wide variety of sectors starting from aerospace and nautical up to more commonly used uses such as bicycles, glasses, and so on. The characteristics that have made these materials popular are mainly their low weight, resistance to fatigue, and corrosion. In contrast to the advantages, however, it should be noted that the manufacturing processes of composite materials are not eco-friendly, and their disposal is rather difficult. For these reasons, in recent decades, the use of natural fibers has gained increasing attention, allowing the development of new materials sharing the same advantages with conventional composite systems while respecting the environment. In this work, the behavior of totally eco-friendly composite materials during flexural tests has been studied through infrared (IR) analysis. IR imaging is a well-known non-contact technique and represents a reliable means of providing low-cost in situ analysis. According to this method, the surface of the sample under investigation is monitored, under natural conditions or after heating, by recording thermal images with an appropriate IR camera. Here, the results achieved for jute- and basalt-based eco-friendly composites through the use of both passive and active IR imaging approaches are reported and discussed, showing the possibilities of use also in an industrial environment.

Active vs. Passive Thermal Imaging for Helping the Early Detection of Soil-Borne Rot Diseases on Wild Rocket [Diplotaxis tenuifolia (L.) D.C.]

Cultivation of wild rocket [Diplotaxis tenuifolia (L.) D.C.] as a baby-leaf vegetable for the high-convenience food chain is constantly growing due to its nutritional and taste qualities. As is well known, these crops are particularly exposed to soil-borne fungal diseases and need to be effectively protected. At present, wild rocket disease management is performed by using permitted synthetic fungicides or through the application of agro-ecological and biological methods that must be optimized. In this regard, the implementation of innovative digital-based technologies, such as infrared thermography (IT), as supporting systems to decision-making processes is welcome. In this work, leaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection. A comparison between the thermal analysis carried out in both medium (MWIR)- and long (LWIR)-wave infrared was made and discussed. The results achieved highlight how the monitoring based on the use of IT is promising for carrying out an early detection of the rot diseases induced by the investigated pathogens, allowing their detection in 3-6 days before the canopy is completely wilted. Active thermal imaging has the potential to detect early soil-borne rotting diseases.

Investigation of Glypican-4 and -6 by Infrared Spectral Imaging during the Hair Growth Cycle

The expression of glypicans in different hair follicle (HF) compartments is still poorly understood. Heparan sulfate proteoglycans (HSPGs) distribution in HF is classically investigated by conventional histology, biochemical analysis, and immunohistochemistry. Our previous study proposed a novel approach to assess hair histology and glypican-1 (GPC1) distribution changes in the HF at different phases of the hair growth cycle using infrared spectral imaging (IRSI). We show in the present manuscript for the first time complementary data on the distribution of glypican-4 (GPC4) and glypican-6 (GPC6) in HF at different phases of the hair growth cycle using IR imaging. Findings were supported by Western blot assays focusing on the GPC4 and GPC6 expression in HFs. Like all proteoglycan features, the glypicans are characterized by a core protein to which sulfated and/or unsulfated glycosaminoglycan (GAG) chains are covalently linked. Our study demonstrates the capacity of IRSI to identify the different HF tissue structures and to highlight protein, proteoglycan (PG), GAG, and sulfated GAG distribution in these structures. The comparison between anagen, catagen, and telogen phases shows the qualitative and/or quantitative evolution of GAGs, as supported by Western blot. Thus, in one analysis, IRSI can simultaneously reveal the location of proteins, PGs, GAGs and sulfated GAGs in HFs in a chemical and label-free manner. From a dermatological point of view, IRSI may constitute a promising technique to study alopecia.

Chemical Gas Telemetry System Based on Multispectral Infrared Imaging

Environmental monitoring, public safety, safe production, and other areas all benefit greatly from the use of gas detection technologies. The infrared image of a gas could be used to determine its type from a long distance in gas detection. The infrared image can show the spatial distribution of the gas cloud and the background, allowing for long-distance and non-contact detection during safety production and hazardous chemical accident rescue. In this study, a gas detection system based on multispectral infrared imaging is devised, which can detect a variety of gases and determine the types of gas by separating the infrared radiation. It is made up of an imaging optical system, an uncooled focal plane detector, a filter controller, and a data gathering and processing system. The resolution of the infrared image is 640 × 512 and the working band of the system is 6.5~15 μm. The system can detect traces of pollutants in ambient air or gas clouds at higher concentrations. Ammonia, sulfur hexafluoride, methane, sulfur dioxide, and dimethyl methyl phosphonate were all successfully detected in real time. Ammonia clouds could be detected at a distance of 1124.5 m.

Dynamic Infrared Imaging of Vitreous Floaters

Background and Objective

To evaluate the use of dynamic infrared (IR) imaging as a tool for the objective evaluation of symptomatic vitreous floaters and to correlate it with the patient symptomatology.

Study Design/Materials and Methods

Retrospective study that examined 66 eyes of 44 patients with symptomatic and asymptomatic vitreous opacities. Patients were imaged using the Heidelberg Spectralis dynamic infrared (IR) image in video mode to record the vitreous movements and shadow artifacts within 30 degrees of the center of the macula. Patients were also asked how symptomatic their vitreous floaters were from absent to severe. After reviewing IR videos and OCT, a grading system was created to evaluate the floaters and a masked reader was asked to evaluate the videos and OCT based on the grading system created.

Results

A total of 66 eyes were identified and examined with the IR videos, 50 were symptomatic, and 16 were asymptomatic. After masked review and analysis of the IR videos, there were 4 characteristics that correlated with the patient's symptoms: size, location, movement, and density of obscuration of the OCT B Scan by the vitreous opacity. A table with grading of these characteristics was created to analyze how symptomatic patients were. A masked grader was asked to grade the videos and OCT using the grading system created. A positive correlation was found between the masked grader and the symptoms of patients. (0.70039; p < 0.00001).

Conclusion

Dynamic IR video capture of vitreous opacities is a new imaging technique that can qualitatively assess vitreous opacities in a way that correlates to a patient's symptoms. This imaging modality can provide a qualitative assessment of the patient's severity of symptoms based on the location, density, and movement of the visualized vitreous opacities in the imaged video.

Eosin Y-Functionalized Upconverting Nanoparticles: Nanophotosensitizers and Deep Tissue Bioimaging Agents for Simultaneous Therapeutic and Diagnostic Applications

Functionalized upconverting nanoparticles (UCNPs) are promising theragnostic nanomaterials for simultaneous therapeutic and diagnostic purposes. We present two types of non-toxic eosin Y (EY) nanoconjugates derived from UCNPs as novel nanophotosensitizers (nano-PS) and deep-tissue bioimaging agents employing light at 800 nm. This excitation wavelength ensures minimum cell damage, since the absorption of water is negligible, and increases tissue penetration, enhancing the specificity of the photodynamic treatment (PDT). These UCNPs are uniquely qualified to fulfil three important roles: as nanocarriers, as energy-transfer materials, and as contrast agents. First, the UCNPs enable the transport of EY across the cell membrane of living HeLa cells that would not be possible otherwise. This cellular internalization facilitates the use of such EY-functionalized UCNPs as nano-PS and allows the generation of reactive oxygen species (ROS) under 800 nm light inside the cell. This becomes possible due to the upconversion and energy transfer processes within the UCNPs, circumventing the excitation of EY by green light, which is incompatible with deep tissue applications. Moreover, the functionalized UCNPs present deep tissue NIR-II fluorescence under 808 nm excitation, thus demonstrating their potential as bioimaging agents in the NIR-II biological window.

Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.

Subwavelength Quasi-Periodic Array for Infrared Antireflection

Infrared antireflection of a zinc sulfide (ZnS) surface is important to improve performance of infrared detector systems. In this paper, double-pulse femtosecond laser micro-machining is proposed to fabricate a subwavelength quasi-periodic array (SQA) on ZnS substrate for infrared antireflection. The SQA consisting of approximately 30 million holes within a 2 × 2 cm² area is uniformly formed in a short time. The double-pulse beam can effectively suppress the surface plasma shielding effect, resulting in obtaining a larger array depth. Further, the SQA depth is tunable by changing pulse energy and pulse delay, and can be used to readily regulate the infrared transmittance spectra as well as hydrophobicity. Additionally, the optical field intensity distributions of the SQA simulated by the rigorous coupled-wave analysis method indicate the modulation effect by the array depth. Finally, the infrared imaging quality captured through an infrared window embedded SQA is evaluated by a self-built infrared detection system.

Classification of Drivers' Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals

Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test-DST and Ray Auditory Verbal Learning Test-RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers' response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers' MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.

Photodetection and Infrared Imaging Based on Cd3As2 Epitaxial Vertical Heterostructures

Due to the nontrivial electronic structure, Cd₃As₂ is predicted to possess various transport properties and outstanding photoresponses. Photodetectors based on topological materials are mostly made up of nanoplates, yet monolithic in situ heteroepitaxial Cd₃As₂ photodetectors are rarely reported to date owing to the crystal mismatch between Cd₃As₂ and semiconductors. Here, we demonstrate Cd₃As₂/Zn_xCd_1-xTe/GaSb vertical heteroepitaxial photodetectors via molecule beam epitaxy. By constructing dual-Schottky junctions, these photodetectors show high responsivity and external quantum efficiency in a broadband spectrum. Based on the strong and fast photoresponse, we achieved visible light to near-infrared imaging using a one-pixel imaging system with a galvo. Our results illustrate that the integration of three-dimensional Dirac semimetal Cd₃As₂ with semiconductors has potential applications in broadband photodetection and infrared cameras.

Preliminary Study of the Mural Paintings of Sotterra Church in Paola (Cosenza, Italy)

A multi-analytical approach was employed to study wall paintings located in the Sotterra church at Paola, in the province of Cosenza, Italy. The site is an underground church (hence the name of Sotterra, which means "under the earth") rediscovered in the second half of the 19th century, during the building works of the Madonna del Carmine church on the same area. This underground church preserves valuable mural paintings having different styles. The construction's dating and overlapped modifications made until the site was abandoned is also debated. A wall painting, depicting "The Virgin" as part of the "Annunciation and the Archangel Gabriel" present on the opposite side of the apse, was selected and investigated using both in situ and laboratory-based analysis. Preliminarily, the non-destructive investigations involved several analytical techniques (IR imaging, UV-Induced Visible Fluorescence, and X-ray Fluorescence analyses) that provided mapping and characterization of pictorial layers and first data about deterioration phenomena. On the basis of this information, a more in-depth study was conducted on micro-fragments aimed at characterizing the stratigraphy and to identify the artist's technique. Cross-sections were analysed using polarized optical microscopy and electron scanning microscopy coupled with energy-dispersive X-ray spectroscopy to obtain morphological and chemical information on the selected pictorial micro-fragments of the wall painting. The results allowed to characterize the pigments and provide better readability of the whole figure, revealing details that are not visible to the naked eye, important for future historical-artistic and conservative studies. The results represent the first step of a systematic archaeometric research aimed at supporting the ongoing historical-stylistic studies to distinguish the different building phases hypothesized for this religious site which remained buried for three centuries.

Towards Mapping Mouse Metabolic Tissue Atlas by Mid-Infrared Imaging with Heavy Water Labeling

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D₂ O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D₂ O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient's consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims' mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies' longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

Multispectral Mid-Infrared Camera System for Accurate Stand-Off Temperature and Column Density Measurements on Flames

Accurate measurement of temperature in flames is a challenging problem that has been successfully addressed by hyperspectral imaging. This technique is able to provide maps of not only temperature T (K) but also of column density Q (ppm·m) of the main chemical species. Industrial applications, however, require cheaper instrumentation and faster and simpler data analysis. In this work, the feasibility and performance of multispectral imaging for the retrieval of T and QCO2 in flames are studied. Both the hyperspectral and multispectral measurement methods are described and applied to a standard flame, with known T and QCO2, and to an ordinary Bunsen flame. Hyperspectral results, based on emission spectra with 0.5 cm−1 resolution, were found in previous works to be highly accurate, and are thus considered as the ground truth to compare with multispectral measurements of a mid-IR camera (3 to 5 μm) with a six interference filter wheel. Maps of T and Q obtained by both methods show that, for regions with T ≳1300 K, the average of relative errors in multispectral measurements is ∼5% for T (and can be reduced to ∼2.5% with a correction based on a linear regression) and ∼20% for Q. Results obtained with four filters are very similar; results with two filters are also similar for T but worse for Q.

Assessment of Ovarian Tumor Growth in Wild-Type and Lumican-Deficient Mice: Insights Using Infrared Spectral Imaging, Histopathology, and Immunohistochemistry

Simple Summary

Lumican, a small leucine-rich proteoglycan (SLRP), maintains extracellular matrix (ECM) integrity while inhibiting melanoma primary tumor development, as well as metastatic spread. The aim of this study was to analyze the effect of lumican on tumor growth of murine ovarian carcinoma. C57BL/6 wild type mice (n = 12) and lumican-deficient mice (n = 10) were subcutaneously injected with murine ovarian epithelial carcinoma ID8 cells, and sacrificed after 18 days. Label-free infrared spectral imaging (IRSI) generated high contrast IR images allowing identification of different ECM regions of the skin and the ovarian tumor. IRSI showed a good correlation with collagen distribution as well as organization, as analyzed using second harmonic generation imaging within the tumor area. The results demonstrated that lumican inhibited the growth of ovarian cancer mainly by altering collagen fibrilogenesis.

Abstract

Ovarian cancer remains one of the most fatal cancers due to a lack of robust screening methods of detection at early stages. Extracellular matrix (ECM) mediates interactions between cancer cells and their microenvironment via specific molecules. Lumican, a small leucine-rich proteoglycan (SLRP), maintains ECM integrity and inhibits both melanoma primary tumor development, as well as metastatic spread. The aim of this study was to analyze the effect of lumican on tumor growth of murine ovarian epithelial cancer. C57BL/6 wild type mice (n = 12) and lumican-deficient mice (n = 10) were subcutaneously injected with murine ovarian epithelial carcinoma ID8 cells, and then sacrificed after 18 days. Analysis of tumor volumes demonstrated an inhibitory effect of endogenous lumican on ovarian tumor growth. The ovarian primary tumors were subjected to histological and immunohistochemical staining using anti-lumican, anti-αv integrin, anti-CD31 and anti-cyclin D1 antibodies, and then further examined by label-free infrared spectral imaging (IRSI), second harmonic generation (SHG) and Picrosirius red staining. The IR tissue images allowed for the identification of different ECM tissue regions of the skin and the ovarian tumor. Moreover, IRSI showed a good correlation with αv integrin immunostaining and collagen organization within the tumor. Our results demonstrate that lumican inhibits ovarian cancer growth mainly by altering collagen fibrilogenesis.

Highly Discriminative Physiological Parameters for Thermal Pattern Classification

Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to 1.8 cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (DMR-IR). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position a=1.6 cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the DMR-IR.

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.

Optimal Material Search for Infrared Markers under Non-Heating and Heating Conditions

Research on optimal markers for infrared imaging and differences in their characteristics in the presence of heat sources has not yet been performed. This study investigates optimal material combinations for developing an accurate and detachable infrared marker for multiple conditions in the medium wave infrared (MWIR) region. Based on four requirements, 11 material combinations are systematically evaluated. Consequently, the optimal marker differs in relation to the presence of specular reflection components. Metal–insulator markers are suitable under non-heating and hot-air heating conditions without reflection components, although a printed marker made of copier paper is captured more clearly than metal–insulator markers during heating, using an optical radiation heating source with reflection components. Our findings can be applied in structural health monitoring and multi-modal projection involving heat sources.

Corrigendum: The near-infrared autofluorescence fingerprint of the brain

In the article by J. Lifante et al (doi: 10.1002/jbio.202000154), published in J. Biophotonics 2020;13:e202000154, a spectral feature corresponding to tissue reflectance was mistakenly attributed to autofluorescence. This corrigendum is published to correct the interpretation of the spectral data and images in the manuscript.

Raman Imaging Reveals Accumulation of Hemoproteins in Plaques from Alzheimer's Diseased Tissues

Hemes have been suggested to play a central role in Alzheimer's disease since they show high peroxidase reactivity when bound to amyloid β peptides, leading to the production of reactive oxygen species. Here we used Fourier transform infrared and Raman imaging on Alzheimer's diseased mice and human brain tissue. Our finding suggests the accumulation of hemes in the senile plaques of both murine and human samples. We compared the Raman signature of the plaques to the ones of various hemeoproteins and to the hemin-Aβ-42 complex. The detected Raman signature of the plaques does not allow identifying the type of heme accumulating in the plaques.

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes' movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes' movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes' movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.

Pilot Clinical Study Investigating the Thermal Physiology of Breast Cancer via High-Resolution Infrared Imaging

This descriptive study investigates breast thermal characteristics in females histologically diagnosed with unilateral breast cancer and in their contralateral normal breasts. The multi-institutional clinical pilot study was reviewed and approved by the Institutional Review Boards (IRBs) at participating institutions. Eleven female subjects with radiologic breast abnormalities were enrolled in the study between June 2019 and September 2019 after informed consent was obtained. Static infrared images were recorded for each subject. The Wilcoxon signed rank test was used to conduct paired comparisons in temperature data between breasts among the eight histologically diagnosed breast cancer subjects (n = 8). Localized temperatures of cancerous breast lesions were significantly warmer than corresponding regions in contralateral breasts (34.0 ± 0.9 °C vs. 33.2 ± 0.5 °C, p = 0.0142, 95% CI 0.25-1.5 °C). Generalized temperatures over cancerous breasts, in contrast, were not significantly warmer than corresponding regions in contralateral breasts (33.9 ± 0.8 °C vs. 33.4 ± 0.4 °C, p = 0.0625, 95% CI -0.05-1.45 °C). Among the breast cancers enrolled, breast cancers elevated temperatures locally at the site of the lesion (localized hyperthermia), but not over the entire breast (generalized hyperthermia).

Fast Quantification of Air Pollutants by Mid-Infrared Hyperspectral Imaging and Principal Component Analysis

An imaging Fourier-transform spectrometer in the mid-infrared (1850-6667 cm-1) has been used to acquire transmittance spectra at a resolution of 1 cm-1 of three atmospheric pollutants with known column densities (Q): methane (258 ppm·m), nitrous oxide (107.5 ppm·m) and propane (215 ppm·m). Values of Q and T have been retrieved by fitting them with theoretical spectra generated with parameters from the HITRAN database, based on a radiometric model that takes into account gas absorption and emission, and the instrument lineshape function. A principal component analysis (PCA) of experimental data has found that two principal components are enough to reconstruct gas spectra with high fidelity. PCA-processed spectra have better signal-to-noise ratio without loss of spatial resolution, improving the uniformity of retrieval. PCA has been used also to speed up retrieval, by pre-calculating simulated spectra for a range of expected Q and T values, applying PCA to them and then comparing the principal components of experimental spectra with those of the simulated ones to find the gas Q and T values. A reduction in calculation time by a factor larger than one thousand is achieved with improved accuracy. Retrieval can be further simplified by obtaining T and Q as quadratic functions of the two first principal components.

Dynamic thermal imaging confirms local but not fast systemic ABA responses

Abscisic acid (ABA) signals regulating stomatal aperture and water loss are usually studied in detached leaves or isolated epidermal peels and at infrequent timepoints. Measuring stomatal ABA responses in attached leaves across a time course enables the study of stomatal behaviour in the physiological context of the plant. Infrared thermal imaging is often used to characterize steady-state stomatal conductance via comparisons of leaf surface temperature but is rarely used to capture stomatal responses over time or across different leaf surfaces. We used dynamic thermal imaging as a robust, but sensitive, tool to observe stomatal ABA responses in a whole plant context. We detected stomatal responses to low levels of ABA in both monocots and dicots and identified differences between the responses of different leaves. Using whole plant thermal imaging, stomata did not always behave as described previously for detached samples: in Arabidopsis, we found no evidence for fast systemic ABA-induced stomatal closure, and in barley, we observed no requirement for exogenous nitrate during ABA-induced stomatal closure. Thus, we recommend dynamic thermal imaging as a useful approach to complement detached sample assays for the study of local and systemic stomatal responses and molecular mechanisms underlying stomatal responses to ABA in the whole plant context.

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

ICG-NIR-guided lymph node dissection during robotic subtotal gastrectomy for gastric cancer. A single-centre experience

BACKGROUND

Near-infrared (NIR) fluorescence imaging with indocyanine green (ICG) allows intraoperative visualisation of the lymph nodes (LNs) draining the tumour.

METHODS

We included in our study 20 patients who underwent robotic subtotal gastrectomy + D2 lymphadenectomy for gastric cancer. In 10 cases, intraoperative ICG-guided lymphography has been used (Group A). We compared the number of LNs retrieved with the use of NIR imaging and the number of LNs retrieved without the use of this technique (Group B, historical group).

RESULTS

No complications related to ICG injection or near-infrared imaging were observed. The mean number of overall LNs retrieved was significantly greater in Group A than in group B (40 vs. 24). No statistically significant difference in operative time was observed.

CONCLUSIONS

ICG-guided fluorescent lymphography can help in performing a more accurate locoregional lymphadenectomy during robotic subtotal gastrectomy for gastric cancer. This technique represents a precious contribution to gastric cancer surgery.

Integrated Structure and Device Engineering for High Performance and Scalable Quantum Dot Infrared Photodetectors

Colloidal quantum dots (CQDs) are emerging as promising materials for the next generation infrared (IR) photodetectors, due to their easy solution processing, low cost manufacturing, size-tunable optoelectronic properties, and flexibility. Tremendous efforts including material engineering and device structure manipulation have been made to improve the performance of the photodetectors based on CQDs. In recent years, benefiting from the facial integration with materials such as 2D structure, perovskite and silicon, as well as device engineering, the performance of CQD IR photodetectors have been developing rapidly. On the other hand, to prompt the application of CQD IR photodetectors, scalable device structures that are compatible with commercial systems are developed. Herein, recent advances of CQD based IR photodetectors are summarized, especially material integration, device engineering, and scalable device structures.

The near-infrared autofluorescence fingerprint of the brain

The brain is a vital organ involved in most of the central nervous system disorders. Their diagnosis and treatment require fast, cost-effective, high-resolution and high-sensitivity imaging. The combination of a new generation of luminescent nanoparticles and imaging systems working in the second biological window (near-infrared II [NIR-II]) is emerging as a reliable alternative. For NIR-II imaging to become a robust technique at the preclinical level, full knowledge of the NIR-II brain autofluorescence, responsible for the loss of image resolution and contrast, is required. This work demonstrates that the brain shows a peculiar infrared autofluorescence spectrum that can be correlated with specific molecular components. The existence of particular structures within the brain with well-defined NIR autofluorescence fingerprints is also evidenced, opening the door to in vivo anatomical imaging. Finally, we propose a rational selection of NIR luminescent probes suitable for low-noise brain imaging based on their spectral overlap with brain autofluorescence.

An Acquisition Method for Visible and Near Infrared Images from Single CMYG Color Filter Array-Based Sensor

Near-infrared (NIR) images are very useful in many image processing applications, including banknote recognition, vein detection, and surveillance, to name a few. To acquire the NIR image together with visible range signals, an imaging device should be able to simultaneously capture NIR and visible range images. An implementation of such a system having separate sensors for NIR and visible light has practical shortcomings due to its size and hardware cost. To overcome this, a single sensor-based acquisition method is investigated in this paper. The proposed imaging system is equipped with a conventional color filter array of cyan, magenta, yellow, and green, and achieves signal separation by applying a proposed separation matrix which is derived by mathematical modeling of the signal acquisition structure. The elements of the separation matrix are calculated through color space conversion and experimental data. Subsequently, an additional denoising process is implemented to enhance the quality of the separated images. Experimental results show that the proposed method successfully separates the acquired mixed image of visible and near-infrared signals into individual red, green, and blue (RGB) and NIR images. The separation performance of the proposed method is compared to that of related work in terms of the average peak-signal-to-noise-ratio (PSNR) and color distance. The proposed method attains average PSNR value of 37.04 and 33.29 dB, respectively for the separated RGB and NIR images, which is respectively 6.72 and 2.55 dB higher than the work used for comparison.

Gallium Phosphide Nanowires in a Free-Standing, Flexible, and Semitransparent Membrane for Large-Scale Infrared-to-Visible Light Conversion

Engineering of nonlinear optical response in nanostructures is one of the key topics in nanophotonics, as it allows for broad frequency conversion at the nanoscale. Nevertheless, the application of the developed designs is limited by either high cost of their manufacturing or low conversion efficiencies. This paper reports on the efficient second-harmonic generation in a free-standing GaP nanowire array encapsulated in a polymer membrane. Light coupling with optical resonances and field confinement in the nanowires together with high nonlinearity of GaP material yield a strong second-harmonic signal and efficient near-infrared (800-1200 nm) to visible upconversion. The fabricated nanowire-based membranes demonstrate high flexibility and semitransparency for the incident infrared radiation, allowing utilizing them for infrared imaging, which can be easily integrated into different optical schemes without disturbing the visualized beam.

Translation of an esophagus histopathological FT-IR imaging model to a fast quantum cascade laser modality

The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT-IR-based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT-IR-based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin-embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.

Non-Contact Evaluation of Pigs' Body Temperature Incorporating Environmental Factors

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature.

Up-Conversion Sensing of 2D Spatially-Modulated Infrared Information-Carrying Beams with Si-Based Cameras

Up-conversion sensing based on optical heterodyning of an IR (infrared) image with a local oscillator laser wave in a nonlinear optical sum-frequency mixing (SFM) process is a practical solution to circumvent some limitations of IR image sensors in terms of signal-to-noise ratio, speed, resolution, or cooling needs in some demanding applications. In this way, the spectral content of an IR image can become spectrally shifted to the visible/near infrared (VIS/NWIR) and then detected with silicon focal plane arrayed sensors (Si-FPA), such as CCD/CMOS (charge-coupled and complementary metal-oxide-semiconductor devices). This work is an extension of a previous study where we recently introduced this technique in the context of optical communications, in particular in FSOC (free-space optical communications). Herein, we present an image up-conversion system based on a 1064 nm Nd³⁺: YVO₄ solid-state laser with a KTP (potassium titanyl phosphate) nonlinear crystal located intra-cavity where a laser beam at 1550 nm 2D spatially-modulated with a binary Quick Response (QR) code is mixed, giving an up-converted code image at 631 nm that is detected with an Si-based camera. The underlying technology allows for the extension of other IR spectral allocations, construction of compact receivers at low cost, and provides a natural way for increased protection against eavesdropping.

Label-Free Infrared Spectral Histology of Skin Tissue Part I: Impact of Lumican on Extracellular Matrix Integrity

Proteoglycans (PG) play an important role in maintaining the extracellular matrix (ECM) integrity. Lumican, a small leucine rich PG, is one such actor capable of regulating such properties. In this study, the integrity of the dermis of lumican-deleted Lum^–/– vs. wild-type mice was investigated by conventional histology and by infrared spectral histology (IRSH). Infrared spectroscopy is a non-invasive, rapid, label-free and sensitive technique that allows to probe molecular vibrations of biomolecules present in a tissue. Our IRSH results obtained on control (WT, n = 3) and Lum^–/– (n = 3) mice showed that different histological structures were identified by using K-means clustering and validated by hematoxylin eosin saffron (HES) staining. Furthermore, an important increase of the dermis thickness was observed in Lum^–/– compared to WT mice. In terms of structural information, analysis of the spectral images also revealed an intra-group homogeneity and inter-group heterogeneity. In addition, type I collagen contribution was evaluated by HES and picrosirius red staining as well as with IRSH. Both techniques showed a strong remodeling of the ECM in Lum^–/– mice due to the looseness of collagen fibers in the increased dermis space. These results confirmed the impact of lumican on the ECM integrity. The loss of collagen fibers organization due to the absence of lumican can potentially increase the accessibility of anti-cancer drugs to the tumor. These results are qualitatively interesting and would need further structural characterization of type I collagen fibers in terms of size, organization, and orientation.

Recent advances in infrared imagers: toward thermodynamic and quantum limits of photon sensitivity

Infrared detection and imaging are key enabling technologies for a vast number of applications, ranging from communication, to medicine and astronomy, and have recently attracted interest for their potential application in optical interconnects and quantum computing. Nonetheless, infrared detection still constitutes the performance bottleneck for several of these applications, due to a number of unsolved challenges, such as limited quantum efficiency, yield and scalability of the devices, as well as limited sensitivity and low operating temperatures. The current commercially dominating technologies are based on planar semiconducting PIN or avalanche detectors. However, recent developments in semiconductor technology and nano-scale materials have enabled significant technological advancement, demonstrating the potential for groundbreaking achievements in the field. We review the recent progress in the most prominent novel detection technologies, and evaluate their advantages, limitations, and prospects. We further offer a perspective on the main fundamental limits on the detectors sensitivity, and we discuss the technological challenges that need to be addressed for significative advancement of the field. Finally, we present a set of potential system-wide strategies, including nanoscale and low-dimensional detectors, light coupling enhancement strategies, advanced read-out circuitry, neuromorphic and curved image sensors, aimed at improving the overall imagers performance.

Modified Distance Transformation for ImageEnhancement in NIR Imaging of Finger Vein System

Most of the current image processing methods used in the near-infrared imaging of fingervascular system concentrate on the extraction of internal structures (veins). In this paper, we proposea novel approach which allows to enhance both internal and external features of a finger. The methodis based on the Distance Transformation and allows for selective extraction of physiological structuresfrom an observed finger. We evaluate the impact of its parameters on the effectiveness of the alreadyestablished processing pipeline used for biometric identification. The new method was comparedwith five state-of-the-art approaches to features extraction (position-gray-profile-curve-PGPGC,maximum curvature points in image profiles-MC, Niblack image adaptive thresholding-NAT,repeated dark line tracking-RDLT, and wide line detector-WD) on the GustoDB database of imagesobtained in a wide range of NIR wavelengths (730-950 nm). The results indicate a clear superiorityof the proposed approach over the remaining alternatives. The method managed to reach over 90%identification accuracy for all analyzed datasets.

Exposure Assessment in Millimeter-Wave Reverberation Chamber Using Murine Phantoms

This study deals with the design and calibration of the first mode-stirred reverberation chamber (RC) in the 60-GHz-band adapted for in vivo bioelectromagnetic studies. In addition to the interface for electromagnetic and thermal dosimetry, the interfaces for lighting and ventilation were integrated into the RC walls while preserving acceptable shielding. The RC with mechanical and electronic steering capabilities is characterized in the 55-65 GHz range. To this end, murine skin-equivalent phantoms of realistic shape were designed and fabricated. Their complex permittivity is within ±12% of the target value of murine skin (6.19-j5.81 at 60 GHz). The quality factor of the RC loaded with an animal cage, bedding litter, and five murine phantoms was found to be 1.2 × 10⁴ . The losses inside the RC were analyzed, and it was demonstrated that the main sources of the power dissipation were the phantoms and mice cage. The input power required to reach the average incident power density of 1 and 5 mW/cm² was found to be 0.23 and 1.14 W, respectively. Surface heating of the mice models was measured in the infrared (IR) range using a specifically designed interface, transparent at IR and opaque at millimeter waves (mmW). Experimental results were compared with an analytical solution of the heat transfer equation and to full-wave computations. Analytical and numerical results were in very good agreement with measurements (the relative deviation after 90 min of exposure was within 4.2%). Finally, a parametric study was performed to assess the impact of the thermophysical parameters on the resulting heating. Bioelectromagnetics. 2020;41:121-135. © 2020 Bioelectromagnetics Society.