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Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation., (© 2024. The Author(s).)

Significance: Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach., Aim: We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated HbO 2 and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes b and c and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO)., Approach: The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in HbO 2 , Hb, oxCCO, and the oxidized state of cytochrome b and c (oxCytb and oxCytc)., Results: The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in HbO 2 , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900 nm. The optimization procedure does not help to resolve changes in cytochrome b and c in a significant way but helps to better resolve oxCCO changes., Conclusions: We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices., (© 2024 The Authors.)

Significance: Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time., Aim: Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification., Approach: We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information., Results: The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at λ = 422    nm , two for HbO 2 at λ = 542    nm and λ = 576    nm , and one for water at λ = 976    nm ., Conclusion: The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification., (© 2024 The Authors.)

Significance: Developing stable, robust, and affordable tissue-mimicking phantoms is a prerequisite for any new clinical application within biomedical optics. To this end, a thorough understanding of the phantom structure and optical properties is paramount., Aim: We characterized the structural and optical properties of PlatSil SiliGlass phantoms using experimental and numerical approaches to examine the effects of phantom microstructure on their overall optical properties., Approach: We employed scanning electron microscope (SEM), hyperspectral imaging (HSI), and spectroscopy in combination with Mie theory modeling and inverse Monte Carlo to investigate the relationship between phantom constituent and overall phantom optical properties., Results: SEM revealed that microspheres had a broad range of sizes with average ( 13.47 ± 5.98 )    μ m and were also aggregated, which may affect overall optical properties and warrants careful preparation to minimize these effects. Spectroscopy was used to measure pigment and SiliGlass absorption coefficient in the VIS-NIR range. Size distribution was used to calculate scattering coefficients and observe the impact of phantom microstructure on scattering properties. The results were surmised in an inverse problem solution that enabled absolute determination of component volume fractions that agree with values obtained during preparation and explained experimentally observed spectral features. HSI microscopy revealed pronounced single-scattering effects that agree with single-scattering events., Conclusions: We show that knowledge of phantom microstructure enables absolute measurements of phantom constitution without prior calibration. Further, we show a connection across different length scales where knowledge of precise phantom component constitution can help understand macroscopically observable optical properties., (© 2024 The Authors.)

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Reflection light forms the core of our visual perception of the world. We can obtain vast information by examining reflection light from biological surfaces, including pigment composition and distribution, tissue structure and surface microstructure. However, because of the limitations in our visual system, the complete information in reflection light, which we term 'reflectome', cannot be fully exploited. For example, we may miss reflection light information outside our visible wavelengths. In addition, unlike insects, we have virtually no sensitivity to light polarization. We can detect non-chromatic information lurking in reflection light only with appropriate devices. Although previous studies have designed and developed systems for specialized uses supporting our visual systems, we still do not have a versatile, rapid, convenient and affordable system for analyzing broad aspects of reflection from biological surfaces. To overcome this situation, we developed P-MIRU, a novel multispectral and polarization imaging system for reflecting light from biological surfaces. The hardware and software of P-MIRU are open source and customizable and thus can be applied for virtually any research on biological surfaces. Furthermore, P-MIRU is a user-friendly system for biologists with no specialized programming or engineering knowledge. P-MIRU successfully visualized multispectral reflection in visible/non-visible wavelengths and simultaneously detected various surface phenotypes of spectral polarization. The P-MIRU system extends our visual ability and unveils information on biological surfaces., (© The Author(s) 2023. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.

Introduction: Microcirculatory alterations are key mechanisms in sepsis pathophysiology leading to tissue hypoxia, edema formation, and organ dysfunction. Hyperspectral imaging (HSI) is an emerging imaging technology that uses tissue-light interactions to evaluate biochemical tissue characteristics including tissue oxygenation, hemoglobin content and water content. Currently, clinical data for HSI technologies in critical ill patients are still limited., Methods and Analysis: TIVITA® Tissue System was used to measure Tissue oxygenation (StO2), Tissue Hemoglobin Index (THI), Near Infrared Perfusion Index (NPI) and Tissue Water Index (TWI) in 25 healthy volunteers and 25 septic patients. HSI measurement sites were the palm, the fingertip, and a suprapatellar knee area. Septic patients were evaluated on admission to the ICU (E), 6 h afterwards (E+6) and three times a day (t3-t9) within a total observation period of 72 h. Primary outcome was the correlation of HSI results with daily SOFA-scores., Results: Serial HSI at the three measurement sites in healthy volunteers showed a low mean variance expressing high retest reliability. HSI at E demonstrated significantly lower StO2 and NPI as well as higher TWI at the palm and fingertip in septic patients compared to healthy volunteers. StO2 and TWI showed corresponding results at the suprapatellar knee area. In septic patients, palm and fingertip THI identified survivors (E-t4) and revealed predictivity for 28-day mortality (E). Fingertip StO2 and THI correlated to SOFA-score on day 2. TWI was consistently increased in relation to the TWI range of healthy controls during the observation time. Palm TWI correlated positively with SOFA scores on day 3., Discussion: HSI results in septic patients point to a distinctive microcirculatory pattern indicative of reduced skin oxygenation and perfusion quality combined with increased blood pooling and tissue water content. THI might possess risk-stratification properties and TWI could allow tissue edema evaluation in critically ill patients., Conclusion: HSI technologies could open new perspectives in microcirculatory monitoring by visualizing oxygenation and perfusion quality combined with tissue water content in critically ill patients - a prerequisite for future tissue perfusion guided therapy concepts in intensive care medicine., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

An inelastic hyperspectral Scheimpflug lidar system was developed for microalgae classification and quantification. The correction for the refraction at the air-glass-water interface was established, making our system suitable for aquatic environments. The fluorescence spectrum of microalgae was extracted by principal component analysis, and seven species of microalgae from different phyla have been classified. It was verified that when the cell density of Phaeocystis globosa was in the range of ${{1}}{{{0}}^4}\sim{{1}}{{{0}}^6}\;{\rm{cell}}\;{\rm{m}}{{\rm{L}}^{- 1}}$, the cell density had a linear relationship with the fluorescence intensity. The experimental results show our system can identify and quantify microalgae, with application prospects for microalgae monitoring in the field environment and early warning of red tides or algal blooms.

Red, green, blue color photography is a mature technology and a powerful tool for the evaluation and understanding of the way an object reflects light and its related optical properties, but color photography fails to give a complete picture of these effects due to its inherent lack of spectral resolution. In this work, we update the L'OREAL reference device for skin color measurement, the Chromasphere, by replacing its current color camera system with an imaging spectrometer. This imaging spectrometer must provide a spatial resolution on par with the previous color cameras and a spectral resolution commensurate with a spectroradiometer while also achieving a time resolution suitable for in vivo studies of the human face. Due to these requirements, common spatial scanning techniques are not suitable for this application, and so we utilized a spectral-scanning approach based on a tunable liquid-crystal birefringent filter. We present the design and performance tests of a working prototype that is capable of measuring the spectrum in each of 4 MP with a nominal spectral resolution of 10 nm across the wavelength range from 420 to 730 nm in a total imaging time of less than 10 s. We cross-compared the spectral and color measurements obtained with this prototype, an industry-standard spectroradiometer, and a charge-coupled device color camera in order to assess the prototype's performance, and the results of this comparison show that our prototype is capable of taking spectral measurements near enough in quality to those of a spectroradiometer to successfully bridge the divide between such devices and conventional color cameras. Doing so, this instrument opens new possibilities for studies of complex in vivo phenomena that neither non-imaging spectrometers nor conventional cameras can pursue.

Background: Postoperative cerebral hyperperfusion syndrome (CHS) may occur after superficial temporal artery (STA)-middle cerebral artery (MCA) bypass for moyamoya disease (MMD). Predicting postoperative CHS is challenging; however, we previously reported the feasibility of using a hyperspectral camera (HSC) for monitoring intraoperative changes in brain surface hemodynamics during STA-MCA bypass., Objective: To investigate the utility of HSC to predict postoperative CHS during STA-MCA bypass for patients with MMD., Methods: Hyperspectral images of the cerebral cortex of 29 patients with MMD who underwent STA-MCA bypass were acquired by using an HSC before and after anastomosis. We then analyzed the changes in oxygen saturation after anastomosis and assessed its correlation with CHS., Results: Five patients experienced transient neurological deterioration several days after surgery. 123I-N-Isopropyl-iodoamphetamine single-photon emission computed tomography scan results revealed an intense, focal increase in cerebral blood flow at the site of anastomosis without any cerebral infarction. Patients with CHS showed significantly increased oxygen saturation (SO2) in the cerebral cortex after anastomosis relative to those without CHS (33 ± 28 vs. 8 ± 14%, p < 0.0001). Receiver operating characteristic analysis results show that postoperative CHS likely occurs when the increase rate of cortical SO2 value is >15% (sensitivity, 85.0%; specificity, 81.3%; area under curve, 0.871)., Conclusions: This study indicates that hyperspectral imaging of the cerebral cortex may be used to predict postoperative CHS in patients with MMD undergoing STA-MCA bypass., (© 2021 S. Karger AG, Basel.)

Purpose: Multiple studies have shown that spectral analysis of tissue autofluorescence can be used as a live indicator for various pathophysiological states of cardiac tissue, including ischemia, ablation-induced damage, or scar formation. Yet today there are no percutaneous devices that can detect autofluorescence signals from inside a beating heart. Our aim was to develop a prototype catheter to demonstrate the feasibility of doing so., Methods and Results: Here we summarize technical solutions leading to the development of a percutaneous catheter capable of multispectral imaging of intracardiac surfaces. The process included several iterations of light sources, optical filtering, and image acquisition techniques. The developed system included a compliant balloon, 355 nm laser irradiance, a high-sensitivity CCD, bandpass filtering, and image acquisition synchronized with the cardiac cycle. It enabled us to capture autofluorescence images from multiple spectral bands within the visible range while illuminating the endocardial surface with ultraviolet light. Principal component analysis and other spectral unmixing post-processing algorithms were then used to reveal target tissue., Conclusion: Based on the success of our prototype system, we are confident that the development of ever more sensitive cameras, recent advances in tunable filters, fiber bundles, and other optical and computational components makes it possible to create percutaneous catheters capable of acquiring hyper or multispectral hypercubes, including those based on autofluorescence, in real-time. This opens the door for widespread use of this methodology for high-resolution intraoperative imaging of internal tissues and organs-including cardiovascular applications.

Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging (HSI), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: "poultry" and "hyperspectral imaging". A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods., (Copyright © 2020. Published by Elsevier Inc.)

Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB is a short cycle disease and is highly destructive under conducive environments. To provide technical support for the rapid detection of the FHB disease, we proposed to develop a new Fusarium disease index (FDI) based on the spectral data of 374-1050 nm. This study was conducted through the analysis of reflectance spectral data of healthy and diseased wheat ears at the flowering and filling stages by hyperspectral imaging technology and the random forest method. The characteristic wavelengths selected were 570 nm and 678 nm for the late flowering stage, 565 nm and 661 nm for the early filling stage, 560 nm and 663 nm for the combined stage (combining both flowering and filling stages) by random forest. FDI at each stage was derived from the wavebands of each corresponding stage. Compared with other 16 existing spectral indices, FDI demonstrated a stronger ability to determine the severity of the FHB disease. Its determination coefficients ( R 2 ) values exceeded 0.90 and the RMSEs were less than 0.08 in the models for each stage. Furthermore, the model for the combined stage performed better when used at single growth stage, but its effect was weaker than that of the models for the two individual growth stages. Therefore, using FDI can provide a new tool to detect the FHB disease at different growth stages in wheat.

Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm. Equipped with a feature extraction and classification algorithm, the proposed system can be used to determine potato plant health with ∼88 % accuracy. This algorithm was also capable of species identification and is demonstrated as being capable of differentiating between rocket, lettuce, and spinach. Results are promising for an entry-level, low-cost hyperspectral imaging solution for agriculture applications.

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation. [ABSTRACT FROM AUTHOR]