Chronic liver diseases pose a significant global health concern, ranking as the 11th leading cause of death worldwide. It often progresses to organ fibrosis and severe complications such as portal hypertension and cirrhosis. Liver transplantation is the most effective treatment for such diseases, however, the persistent shortage of donors highlights the need for alternatives. Radiofrequency ablation (RFA) is a promising alternative since it is a minimally invasive procedure. RFA uses heat to destroy abnormal tissues. Its benefits include reduced recovery time compared to surgery, precise targeting of affected areas, and long-lasting symptom relief in many cases. However, RFA has challenges, such as potential risks of nerve damage, infection, or incomplete ablation, requiring repeat treatments. Although significant progress in RFA techniques, effective monitoring remains challenging due to the limited ability to accurately characterize the dynamic thermal diffusion and complex tissue responses.

To address this challenge, hyperspectral imaging (HSI) shows promise in monitoring tissue necrosis post-ablation. Our study evaluated HSI's efficacy in monitoring RFA on ex vivo human fibrotic liver tissue samples.

Statistical analysis revealed correlations between spectral patterns and tissue conditions, which helped identify the optimal spectral bands of 543 nm and 579 nm for accurately distinguishing different tissue states. Analyzing the hemoglobin absorption profile indicated significant reductions in absorption of the green light band, showing approximately 40 % reduction in fibrotic tissue and around 20 % reduction in ablated tissue when compared to normal liver tissue. Additionally, a threshold was established for predicting the ablated area of liver samples, ensuring a condition of 90 % specificity.

Consequently, HSI proved to be a valuable tool for monitoring ablation and a step for improving treatment outcomes for liver fibrosis.

Hyperspectral imaging (HSI) and machine learning (ML) have been employed in the medical field for classifying highly infiltrative brain tumours. Although existing HSI databases of in vivo human brains are available, they present two main deficiencies. First, the amount of labelled data are scarce, and second, 3D-tissue information is unavailable. To address both issues, we present the SLIMBRAIN database, a multimodal image database of in vivo human brains that provides HS brain tissue data within the 400-1000 nm spectra, as well as RGB, depth and multiview images. Two HS cameras, two depth cameras and different RGB sensors were used to capture images and videos from 193 patients. All the data in the SLIMBRAIN database can be used in a variety of ways, for example, to train ML models with more than 1 million HS pixels available and labelled by neurosurgeons, to reconstruct 3D scenes or to visualize RGB brain images with different pathologies, offering unprecedented flexibility for both the medical and engineering communities.

Skin lesions associated with head and neck carcinomas present a diagnostic challenge. Conventional imaging methods, such as dermoscopy and RGB imaging, often face limitations in providing detailed information about skin lesions and accurately differentiating tumor tissue from healthy skin.

This study developed a novel approach utilizing tissue index images derived from hyperspectral imaging (HSI) in combination with machine learning (ML) classifiers to enhance lesion classification. The primary aim was to identify essential features for categorizing tumor, peritumor, and healthy skin regions using both RGB and hyperspectral data. Detailed skin lesion images of 16 patients, comprising 24 lesions, were acquired using HSI. The first- and second-order statistics radiomic features were extracted from both the tissue index images and RGB images, with the minimum redundancy-maximum relevance (mRMR) algorithm used to select the most relevant ones that played an important role in improving classification accuracy and offering insights into the complexities of skin lesion morphology. We assessed the classification accuracy across three scenarios: using only RGB images (Scenario I), only tissue index images (Scenario II), and their combination (Scenario III).

The results indicated an accuracy of 87.73% for RGB images alone, which improved to 91.75% for tissue index images. The area under the curve (AUC) for lesion classifications reached 0.85 with RGB images and over 0.94 with tissue index images.

These findings underscore the potential of utilizing HSI-derived tissue index images as a method for the non-invasive characterization of tissues and tumor analysis.

Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility.

To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC).

Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision.

This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system's ability to reliably detect tumor regions and showing high concordance with histopathological findings.

The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.

Hyperspectral imaging and Neural Radiance Field (NeRF) can be combined in powerful ways. With limited hyperspectral images, NeRF can generate images of objects with spectral information from arbitrary viewpoints, which can effectively mitigate defects such as long acquisition time and difficulty in obtaining hyperspectral images. This paper addresses challenges in the application of NeRF methods in the hyperspectral domain, including local errors in convergence caused by noise. Leveraging the characteristics of hyperspectral data, we propose a neural radiance field method employing a multi-channel volume density distribution function. This approach alleviates issues during the generation of neural radiance fields from hyperspectral data, enhancing the robustness of hyperspectral neural radiance field methods across various scenarios, which can help downstream tasks such as discriminating objects more effectively than RGB methods. Experiments demonstrate that the proposed method generates superior hyperspectral images under diverse conditions, with a maximum PSNR 37.66 and a maximum SSIM 0.982.

Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete resection (R0) is crucial, but current methods, relying on white light endoscopy and microscopy, have limitations. Hyperspectral imaging (HSI) offers potential benefits by capturing detailed spectral information beyond human vision.

This study enrolled 32 patients with head and neck squamous cell carcinoma (HNSCC). Following surgical resection specimens underwent ex vivo HSI imaging. Annotated regions were utilized to train a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). Imaging parameters were carefully optimized for efficiency.

Our HSI imaging setup required around 12 min per measurement and demonstrated feasibility with promising accuracy. The combination of HSI and artificial intelligence (AI) achieved an 86% accuracy in predicting tumor tissue. Challenges included data volume and extended capture times.

Hyperspectral imaging, complemented by AI, shows promise in enhancing tissue differentiation for HNSCC. The study envisions real-time integration of HSI into surgery for margin assessment. Challenges such as data volume and capture times warrant further exploration, emphasizing the need for ongoing investigations to refine clinical applications.

Background/Objectives: Our aim was to assess the feasibility of hyperspectral imaging (HSI) to detect changes in tissue oxygenation (StO2) of the back, as non-invasive spinal cord collateral network (CN) monitoring during staged endovascular repair (ER) of thoracoabdominal aortic aneurysm (TAAA). Methods: Between September 2019 and June 2021, 20 patients were treated for TAAA and underwent HSI. They were randomized 1:1 to minimally invasive staged segmental artery coil embolization (MIS2ACE) (n = 10) and staged stentgraft implantation (n = 10) as priming methods. HSI of paravertebral regions was taken during each procedure and up to 10 days after. The primary endpoint was the identification of StO2 changes after ER of TAAA. Results: TAAA Crawford Type II (n = 17) and Type III (n = 3) were treated. After stentgrafting, StO2 increased immediately (p < 0.001), followed by a decrease after 5 days (p < 0.001) and 10 days (p = 0.028). StO2 was significantly higher in the thoracic compared to the lumbar region. There was no significant difference between MIS2ACE and the first stentgrafting for StO2 (p = 0.491). Following MIS2ACE, definitive ER caused a significant decrease in StO2 after 5 days (p = 0.021), which recovered to baseline after 10 days (p = 0.130). After stentgraft priming, definitive ER caused a significant decrease in StO2 after 24 h (p = 0.008), which did not return to baseline after 5 (p < 0.001) and 10 days (p = 0.019). Conclusions: HSI detected significant changes in StO2 in the thoracic and lumbar paravertebral regions during ER of TAAA. These preliminary data suggest the efficacy of MIS2ACE in priming the CN before ER of TAAA.

Uterine fibroids are non-cancerous growths of the uterus that affect nearly 70%-80% of women in their lifetimes. Fibroids can cause severe pain, bleeding, and infertility. The main risk of recurrence is smaller fibroids, which are notoriously hard to detect, being missed during a surgical removal procedure, only to enlarge afterwards. In this work, hyperspectral imaging (HSI) datasets were acquired from samples from 10 patients after receiving a hysterectomy. Optical properties including absorption, scattering, and spectral morphology were extracted and fed into machine learning to classify regions as fibroid and myometrium. Top extracted optical features had significant contrast between fibroid and myometrium (p < 0.0001) and were used to train Random Forest (AUC: 0.9985 ± 0.001, Sensitivity: 0.9534 ± 0.019, Specificity: 0.9936 ± 0.009) and Logistic Regression (AUC: 0.9397 ± 0.013, Sensitivity: 0.8405 ± 0.023, Specificity: 0.8895 ± 0.032) with strong performance across testing splits. With HSI, there is contrast between fibroid and myometrium in the human uterus.

The definitive treatment for pituitary adenoma is transsphenoidal surgical resection. Conventional white light imaging shows limited contrast between the adenoma and the pituitary gland, and only the tissue surface is visualized, leaving a pressing unmet need for improved intraoperative adenoma delineation to preserve pituitary function during surgery.

To evaluate the potential of multispectral imaging to enhance visualization of adenoma during transsphenoidal resection.

A multispectral camera based on a spectrally resolved detector array was coupled to a standard 4-mm rigid endoscope for in vivo imaging, such that the camera head could easily be switched with the standard of care camera head during surgery.

The multispectral imaging (MSI) endoscope was deployed during transsphenoidal surgery, and usable data were obtained from 12 patients. MSI was able to distinguish between an adenoma and a healthy pituitary based on the spectral angle with the reference spectrum of blood.

The MSI endoscope holds the potential to differentiate adenoma tissue and healthy pituitary. With further development, MSI endoscopy could enable real-time label-free delineation of tumors during surgery, based on quantitative thresholds, which should contribute to improving the completeness of resection, while helping to preserve the pituitary gland, preventing serious life-changing complications.

Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, and standardized management are of significant clinical importance for extending the survival rates of brain tumor patients. Artificial intelligence (AI), a discipline within computer science, is leveraging its robust capacity for information identification and combination to revolutionize traditional paradigms of oncology care, offering substantial potential for precision medicine. This article provides an overview of the current applications of AI in brain tumors, encompassing the primary AI technologies, their working mechanisms and working workflow, the contributions of AI to brain tumor diagnosis and treatment, as well as the role of AI in brain tumor scientific research, particularly in drug innovation and revealing tumor microenvironment. Finally, the paper addresses the existing challenges, potential solutions, and the future application prospects. This review aims to enhance our understanding of the application of AI in brain tumors and provide valuable insights for forthcoming clinical applications and scientific inquiries.

This study presents an approach to macroalgae monitoring using a cost-effective hyperspectral imaging (HSI) system and artificial intelligence (AI). Kelp beds are vital habitats and support nutrient cycling, making ongoing monitoring crucial amid environmental changes. HSI emerges as a powerful tool in this context, due to its ability to detect pigment-characteristic fingerprints that are often missed altogether by standard RGB cameras. Still, the high costs of these systems are a barrier to large-scale deployment for in situ monitoring. Here, we showcase the development of a cost-effective HSI setup that combines a GoPro camera with a continuous linear variable spectral bandpass filter. We empirically validate the operational capabilities through the analysis of two brown macroalgae, Fucus serratus and Fucus versiculosus, and two red macroalgae, Ceramium sp. and Vertebrata byssoides, in a controlled aquatic environment. Our HSI system successfully captured spectral information from the target species, which exhibit considerable similarity in morphology and spectral profile, making them difficult to differentiate using traditional RGB imaging. Using a one-dimensional convolutional neural network, we reached a high average classification precision, recall, and F1-score of 99.9%, 89.5%, and 94.4%, respectively, demonstrating the effectiveness of our custom low-cost HSI setup. This work paves the way to achieving large-scale and automated ecological monitoring.

The aim of this study is to validate an approach to monitor the spatial and temporal hemodynamics of intraperitoneal organs using a commercially available laparoscopic system. The approach to create a spatial map of tissue oxygen saturation (StO2) and total hemoglobin concentration (CHbT) is based on a multiple regression model using Monte Carlo simulation of light transport in tissues to specify relationships between RGB values, oxygenated hemoglobin concentration, and deoxygenated hemoglobin concentration. Experiments with an optical phantom are performed to confirm the ability of the approach to detect changes in StO2 and CHbT under different working distances of the endoscope that may occur during actual surgery. In vivo experiments in rats confirm that the proposed approach can quantitatively monitor changes in StO2 and CHbT induced in the small intestine, liver, and cecum. The proposed approach has the potential as a tool for monitoring intraperitoneal organs in real time during laparoscopy.

Hyperspectral imaging (HSI) has evolved from its origins in space missions to become a promising sensing technology for mobile ground robots, offering unique capabilities in material identification and scene understanding. This review examines the integration and applications of HSI systems in ground-based mobile platforms, with emphasis on outdoor implementations. The analysis covers recent developments in two main application domains: autonomous navigation and inspection tasks. In navigation, the review explores HSI applications in Advanced Driver Assistance Systems (ADAS) and off-road scenarios, examining how spectral information enhances environmental perception and decision making. For inspection applications, the investigation covers HSI deployment in search and rescue operations, mining exploration, and infrastructure monitoring. The review addresses key technical aspects including sensor types, acquisition modes, and platform integration challenges, particularly focusing on environmental factors affecting outdoor HSI deployment. Additionally, it analyzes available datasets and annotation approaches, highlighting their significance for developing robust classification algorithms. While recent advances in sensor design and processing capabilities have expanded HSI applications, challenges remain in real-time processing, environmental robustness, and system cost. The review concludes with a discussion of future research directions and opportunities for advancing HSI technology in mobile robotics applications.

This work presents a hyperspectral imager sensitive to radiation in the 1250-666 cm-1 wavenumber range (wavelengths between 8 and 15 μm). The system combines a low-order scanning Fabry-Pérot interferometer with a thermal camera utilizing a 1024 × 768-pixel uncooled microbolometer detector. The compact interferometer design enables a relatively small footprint, providing a spectral resolution between 26 and 39 cm-1, depending on the wavenumber. Transmission measurements of various substances are shown to produce distinct interferograms, facilitating material identification. In addition, a generalized matrix method is used to estimate the relationship between physical cavity length and wavenumber of the incident light, enabling the prediction of interferogram shapes.

Enhancing the sensitivity of biomedical spectroscopy is crucial for advancing medical research and diagnostics. Metasurfaces have emerged as powerful platforms for enhancing the sensitivity of various biomedical spectral detection technologies. This capability arises from their unparalleled ability to improve interactions between light and matter through the localization and enhancement of light fields. In this article, we review representative approaches and recent advances in metasurface-enhanced biomedical spectroscopy. We provide a comprehensive discussion of various biomedical spectral detection technologies enhanced by metasurfaces, including infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, and other spectral modalities. We demonstrate the advantages of metasurfaces in improving detection sensitivity, reducing detection limits, and achieving rapid biomolecule detection while discussing the challenges associated with the design, preparation, and stability of metasurfaces in biomedical detection procedures. Finally, we explore future development trends of metasurfaces for enhancing biological detection sensitivity and emphasize their wide-ranging applications.

Diabetes mellitus (DM) may lead to microvascular and macrovascular complications. Screening for these complications is crucial, and so non-invasive methods with high-dissemination potential are needed. Diabetic peripheral neuropathy (DPN) is particularly challenging to screen due to the lack of reliable clinical markers and endpoints. In this context, Raster Scan Optoacoustic Mesoscopy (RSOM) emerges as a highly promising technique that offers hybrid, non-invasive imaging of optical absorption using light-induced ultrasound waves within tissue without the use of contrast agents. RSOM provides high-resolution visualisation of micro-vasculature and other tissue structures along with functional information. The technique has already assessed microvasculature loss as a function of diabetes progression and used it to characterise DPN severity. RSOM has also shown that cutaneous vessels in the mesoscopic range (mean diameters of 30-40 µm) are most prominently affected by DM and that the mean number of cutaneous vessels was lower in subjects with DM than in healthy participants (p < 0.001 and p < 0.05, respectively). Although experience is still limited, we present an overview of the novel technique in relation to its potential for detecting early DM onset and development of microvascular complications.

Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms.

Lung cancer remains a major global health challenge, and accurate pathological examination is crucial for early detection. This study aims to enhance hyperspectral pathological image analysis by refining annotations at the cell level and creating a high-quality hyperspectral dataset of lung tumors. We address the challenge of coarse manual annotations in hyperspectral lung cancer datasets, which limit the effectiveness of deep learning models requiring precise labels for training. We propose a semi-automated annotation refinement method that leverages hyperspectral data to enhance pathological diagnosis. Specifically, we employ K-means unsupervised clustering combined with human-guided selection to refine coarse annotations into cell-level masks based on spectral features. Our method is validated using a hyperspectral lung squamous cell carcinoma dataset containing 65 image samples. Experimental results demonstrate that our approach improves pixel-level segmentation accuracy from 77.33% to 92.52% with a lower level of prediction noise. The time required to accurately label each pathological slide is significantly reduced. While pixel-level labeling methods for an entire slide can take over 30 mins, our semi-automated method requires only about 5 mins. To enhance visualization for pathologists, we apply a conservative post-processing strategy for instance segmentation. These results highlight the effectiveness of our method in addressing annotation challenges and improving the accuracy of hyperspectral pathological analysis.

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

Pain affects millions worldwide, posing significant challenges in diagnosis and treatment. Despite advances in understanding pain mechanisms, there remains a critical need for validated biomarkers to enhance diagnosis, prognostication, and personalized therapy. This review synthesizes recent advancements in identifying and validating acute and chronic pain biomarkers, including imaging, molecular, sensory, and neurophysiological approaches. We emphasize the emergence of composite, multimodal strategies that integrate psychosocial factors to improve the precision and applicability of biomarkers in chronic pain management. Neuroimaging techniques like MRI and positron emission tomography provide insights into structural and functional abnormalities related to pain, while electrophysiological methods like electroencepholography and magnetoencepholography assess dysfunctional processing in the pain neuroaxis. Molecular biomarkers, including cytokines, proteomics, and metabolites, offer diagnostic and prognostic potential, though extensive validation is needed. Integrating these biomarkers with psychosocial factors into clinical practice can revolutionize pain management by enabling personalized treatment strategies, improving patient outcomes, and potentially reducing healthcare costs. Future directions include the development of composite biomarker signatures, advances in artificial intelligence, and biomarker signature integration into clinical decision support systems. Rigorous validation and standardization efforts are also necessary to ensure these biomarkers are clinically useful. Large-scale collaborative research will be vital to driving progress in this field and implementing these biomarkers in clinical practice. This comprehensive review highlights the potential of biomarkers to transform acute and chronic pain management, offering hope for improved diagnosis, treatment personalization, and patient outcomes.

Hyperspectral imaging (HSI) allows for the contactless analysis of the composition of substances based on the reflected light and is already used in various areas of medicine. The carboxyhemoglobin (CO-Hb) concentration in blood of suspected fire victims serves to prove vitality and the cause of death. However, this metric is usually determined by spectrophotometry in the laboratory. The present study provides the basis for the future development of methods for determining CO-Hb concentrations right at the scene of a corpse or at necropsy using mobile HSI. Human erythrocyte concentrate was mixed with gaseous carbon monoxide using an aerator to produce a series of samples, which were analyzed for their CO-Hb concentration (2.9; 9.7; 18; 27.9; 39.9; 51.9; 62.3; 73.4% CO-Hb) using established spectrophotometric blood gas analysis. These blood samples were stored in a cool place at 4 °C, dripped onto a spot plate every 7 days over a period of 6 weeks, and photographed under standardized conditions (ambient lighting, distance and angle of the camera to the sample, camera settings) using the HSI camera SPECIM IQ. This device analyzes each image in the wavelength range from 400 to 1000 nm in 204 spectral bands. The data sets were used to train a lasso regression model, which provides predicted values for the CO-Hb concentration of the blood sample based on their hyperspectral properties. The results were then compared with the results of spectrophotometric measurements. The lasso regression model allowed the prediction of the CO-Hb concentration of the samples with a mean prediction error of 4.46 percentage points, independent of the sample age. Further investigations regarding pre-analytical influencing factors such as variable ambient light and tissue scattering effects, are planned to validate the robustness of the method and realize practical implementations.

Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3 cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues.

We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work.

We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions.

We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development.

We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

The editors introduce a Journal of Biomedical Optics (JBO) special section on Hyperspectral Imaging. The 2-part special section features a number of important research and review papers on new hyperspectral imaging and detection devices and associated technologies, for application in the areas of translational research and clinical studies.

Xylazine, increasingly implicated in illicit opioid overdose deaths, poses a significant public health threat due to its synergistic effects with fentanyl and resistance to naloxone reversal. Despite its rising prevalence, xylazine is not classified as a controlled substance, leading to its exclusion from routine forensic screening. This study introduces a novel analytical method combining Raman hyperspectral imaging with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to detect xylazine in drug mixtures containing common excipients such as acetaminophen, dipyrone, and mannitol. Utilizing only non-negativity constraints, MCR-ALS successfully resolved the Raman spectrum of xylazine at levels as low as 5 % without reference spectra. The method demonstrated robust performance, with percent variance explained (R²) values of 99.60 %, 99.80 %, and 99.91 % for the drug mixtures containing 25 %, 10 %, and 5 % xylazine, respectively.

I explore hyperspectral imaging, a rapid and noninvasive technique with significant potential in biometrics and medical diagnosis. Personal identification was performed using cross-sectional hyperspectral images of palms, offering a simpler and more robust method than conventional vascular pattern identification methods.

I aim to demonstrate the potential of local cross-sectional hyperspectral palm images to identify individuals with high accuracy.

Hyperspectral imaging of palms, artificial intelligence (AI)-based region of interest (ROI) detection, feature vector extraction, and dimensionality reduction were utilized to validate personal identification accuracy using the area under the curve (AUC) and equal error rate (EER).

The feature vectors extracted by the proposed method demonstrated higher intra-cluster similarity when the clustering data were reduced through uniform manifold approximation and projection compared with principal component analysis and t -distributed stochastic neighbor embedding. A maximum AUC of 0.98 and an EER of 0.04% were observed.

I proposed a biometric method using cross-sectional hyperspectral imaging of human palms. The procedure includes AI-based ROI detection, feature extraction, dimension reduction, and intra- and inter-subject matching using Euclidean distances as a discriminant function. The proposed method has the potential to identify individuals with high accuracy.

Developments of anti-gametocyte drugs have been delayed due to insufficient understanding of gametocyte biology. We report a systematic workflow of data processing algorithms to quantify changes in the absorption spectrum and cell morphology of single malaria-infected erythrocytes. These changes may serve as biomarkers instrumental for the future development of antimalarial strategies, especially for anti-gametocyte drug design and testing. Image-based biomarkers may also be useful for nondestructive, label-free malaria detection and drug efficacy evaluation in resource-limited communities.

We extend the application of hyperspectral microscopy to provide detailed insights into gametocyte stage progression through the quantitative analysis of absorbance spectra and cell morphology in malaria-infected erythrocytes.

Malaria-infected erythrocytes at asexual and different gametocytogenesis stages were imaged through hyperspectral confocal microscopy. The preprocessing of the hyperspectral data cubes to transform them to color images and spectral angle mapper (SAM) analysis were first used to segment hemoglobin (Hb)- and hemozoin (Hz)-abundant areas within the host erythrocytes. Correlations between changes in cell morphology and increasing Hz-abundant areas of the infected erythrocytes were then examined to test their potential as optical biomarkers to determine the progression of infection, involving transitions from asexual to various gametocytogenesis stages.

Following successful segmentation of Hb- and Hz-abundant areas in malaria-infected erythrocytes through SAM analysis, a modest correlation between the segmented Hz-abundant area and cell shape changes over time was observed. A significant increase in both the areal fraction of Hz and the ellipticity of the cell confirms that the Hz fraction change correlates with the progression of gametocytogenesis.

Our workflow enables the quantification of changes in host cell morphology and the relative contents of Hb and Hz at various parasite growth stages. The quantified results exhibit a trend that both the segmented areal fraction of intracellular Hz and the ellipticity of the host cell increase as gametocytogenesis progresses, suggesting that these two metrics may serve as useful biomarkers to determine the stage of gametocytogenesis.

Skin homeostasis is strongly dependent on its hydration levels, making skin water content measurement vital across various fields, including medicine, cosmetology, and sports science. Noninvasive diagnostic techniques are particularly relevant for clinical applications due to their minimal risk of side effects. A range of optical methods have been developed for this purpose, each with unique physical principles, advantages, and limitations. This review provides an in-depth examination of optical techniques such as diffuse reflectance spectroscopy, optoacoustic spectroscopy, optoacoustic tomography, hyperspectral imaging, and Raman spectroscopy. We explore their efficacy in noninvasive monitoring of skin hydration and edema, which is characterized by an increase in interstitial fluid. By comparing the parameters, sensitivity, and clinical applications of these techniques, this review offers a comprehensive understanding of their potential to enhance diagnostic precision and improve patient care.

Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal resolution at the expense of the spatio-spectral resolution, allowing for the observation of temporal events. Designing, realising, and deploying such technologies is yet challenging, particularly due to the lack of clear, user-meaningful quality criteria across diverse applications, sensor types, and workflows. Key research gaps include optimising raw image processing from snapshot spectral imagers and assessing spectral image and video quality in ways valuable to end-users, manufacturers, and developers. This paper identifies several challenges and current opportunities. It proposes considering them jointly and suggests creating a new unified snapshot spectral imaging paradigm that would combine new systems and standards, new algorithms, new cost functions, and quality indices.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Hyperspectral imaging (HSI) technology, which offers both spatial and spectral information, holds significant potential for enhancing diagnostic performance during endoscopy and other medical procedures. However, quantitative evaluation of HSI cameras is challenging due to various influencing factors (e.g., light sources, working distance, and illumination angle) that can alter the reflectance spectra of the same target as these factors vary. Towards robust, universal test methods, we evaluated several data normalization methods aimed at minimizing the impact of these factors. Using a high-resolution HSI camera, we measured the reflectance spectra of diffuse reflectance targets illuminated by two different light sources. These spectra, along with the reference spectra from the target manufacturer, were normalized with nine different methods (e.g., area under the curve, standard normal variate, and centering power methods), followed by a uniform scaling step. We then compared the measured spectra to the reference to evaluate the capability of each normalization method in ensuring a consistent, standardized performance evaluation. Our results demonstrate that normalization can mitigate the impact of some factors during HSI camera evaluation, with performance varying across methods. Generally, noisy spectra pose challenges for normalization methods that rely on limited reflectance values, while methods based on reflectance values across the entire spectrum (such as standard normal variate) perform better. The findings also suggest that absolute reflectance spectral measurements may be less effective for clinical diagnostics, whereas normalized spectral measurements are likely more appropriate. These findings provide a foundation for standardized performance testing of HSI-based medical devices, promoting the adoption of high-quality HSI technology for critical applications such as early cancer detection.

The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic.

A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted.

Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies.

The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.

The chimeric antigen receptor T cell (CAR T) therapy has shown remarkable results in treating certain cancers. It involves genetically modifying a patient's T cells to recognize and attack cancer cells. Despite its potential, CAR T cell therapy is complex and costly and requires the integration of multiple technologies and specialized equipment. Further research is needed to achieve the maximum potential of CAR T cell therapies and to develop effective and efficient methods for their production. This paper presents an overview of current measurement methods used in the key steps of the production of CAR T cells. The study aims to assess the state of the art in monitoring solutions and identify their potential for online monitoring. The results of this paper contribute to the understanding of measurement methods in CAR T cell manufacturing and identify areas where on-line monitoring can be improved. Thus, this research facilitates progress toward the development of effective monitoring of CAR T cell therapies.

Snapshot Mosaic Hyperspectral Cameras (SMHCs) are popular hyperspectral imaging devices for acquiring both color and motion details of scenes. However, the narrow-band spectral filters in SMHCs may negatively impact their motion perception ability, resulting in blurry SMHC frames. In this paper, we propose a hardware-software collaborative approach to address the blurring issue of SMHCs. Our approach involves integrating SMHCs with neuromorphic event cameras for efficient event-enhanced SMHC frame deblurring. To achieve spectral information recovery guided by event signals, we formulate a spectral-aware Event-based Double Integral (sEDI) model that links SMHC frames and events from a spectral perspective, providing principled model design insights. Then, we develop a Diffusion-guided Noise Awareness (DNA) training framework that utilizes diffusion models to learn noise-aware features and promote model robustness towards camera noise. Furthermore, we design an Event-enhanced Hyperspectral frame Deblurring Network (EvHDNet) based on sEDI, which is trained with DNA and features improved spatial-spectral learning and modality interaction for reliable SMHC frame deblurring. Experiments on both synthetic data and real data show that the proposed DNA + EvHDNet outperforms state-of-the-art methods on both spatial and spectral fidelity. The code and dataset will be made publicly available.

Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).

We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.

We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model.

The proposed model was tested on simulated and in vivo data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm.

Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. The gold standard for diagnosis is tissue biopsy during colonoscopy and subsequent histopathology. Limitations of current techniques include the turnaround time required for histopathology and the limited ability to detect flat lesions due to inadequate contrast provided by traditional white light endoscopy (WLE). The focus of this work was to assess detection accuracy for differentiating CRC and noncancerous tissues using excitation-scanning hyperspectral imaging (Ex-HSI) of autofluorescence compared to current diagnostic methods. Fluorescence Ex-HSI permits detection of all emitted light above a cut-off wavelength. Ex-HSI has been shown to reduce acquisition time, improve signal-to-noise ratio, and increase spectral information compared to emission-scanning HSI. This study utilized a mouse CRC model in which Azoxymethane/Dextran sodium sulfate (AOM/DSS) treatments induced colitis with subsequent nodule formation. Ex-HSI images were validated using transmitted light images, confocal "z-stack" images, and histology sectioning with H&E staining. Ex-HSI images were corrected to a flat spectral response, and excitation spectra were extracted from selected regions within each field of view (FOV). Inflammation and rectal bleeding were observed in the initial 31-day timepoint consistent with the AOM/DSS treatment. Colorectal nodules were visible using 4x and 20x magnification objectives and confocal "z-stack" imaging. Extracted spectra displayed two to several peak excitation wavelengths, likely indicating the presence of multiple autofluorescent molecules. Further investigation will utilize principal component analysis (PCA) and convolutional neural networks (CNN) to assess detection performance.

This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis.

The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination.

The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI.

The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer.

In this study, we expanded upon traditional red-green-blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC).

An additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively.

The HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer.

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

To obtain a high-resolution hyperspectral image (HR-HSI), fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) is a prominent approach. Numerous approaches based on convolutional neural networks (CNNs) have been presented for hyperspectral image (HSI) and multispectral image (MSI) fusion. Nevertheless, these CNN-based methods may ignore the global relevant features from the input image due to the geometric limitations of convolutional kernels. To obtain more accurate fusion results, we provide a spatial-spectral transformer-based U-net (SSTF-Unet). Our SSTF-Unet can capture the association between distant features and explore the intrinsic information of images. More specifically, we use the spatial transformer block (SATB) and spectral transformer block (SETB) to calculate the spatial and spectral self-attention, respectively. Then, SATB and SETB are connected in parallel to form the spatial-spectral fusion block (SSFB). Inspired by the U-net architecture, we build up our SSTF-Unet through stacking several SSFBs for multiscale spatial-spectral feature fusion. Experimental results on public HSI datasets demonstrate that the designed SSTF-Unet achieves better performance than other existing HSI and MSI fusion approaches.

This study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in-house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metrics. Transformer models exhibit superior performance in terms of RMSE, SAM, PSNR and SSIM by effectively integrating spatial information to predict accurate spectral profiles, encompassing both visible and extended spectral ranges. Qualitative assessments demonstrate the capability to predict spectral profiles critical for informed surgical decision-making during procedures. Challenges associated with capturing both the visible and extended hyperspectral ranges are highlighted using the MAE, emphasizing the complexities involved. The findings open up the new research direction of hyperspectral reconstruction for surgical applications and clinical use cases in real-time surgical environments.

In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov-Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neural networks with their KAN-based counterparts. Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). Experiments conducted using seven publicly available hyperspectral datasets demonstrated a substantial improvement in classification accuracy across all the networks. The best classification quality was achieved using a KAN-based transformer architecture.

The direct detection of singlet-state oxygen (1O2) constitutes the holy grail dosimetric method for type-II photodynamic therapy (PDT), a goal that can be quantified using multispectral singlet oxygen near-infrared luminescence dosimetry (MSOLD). The optical properties of tissues, specifically their scattering and absorption coefficients, play a crucial role in determining how the treatment and luminescence light are attenuated. Variations in these properties can significantly impact the spatial distribution of the treatment light and hence the generation of singlet oxygen and the detection of singlet oxygen luminescence signals. In this study, we investigated the impact of varying optical properties on the detection of 1O2 luminescence signals during Photofrin-mediated PDT in tissue-mimicking phantoms. For comparison, we also conducted Monte Carlo (MC) simulations under the same conditions. The experimental and simulations are substantially equivalent. This study advances the understanding of MSOLD during PDT.