METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

Large-Scale Analysis of Defects in Atomically Thin Semiconductors using Hyperspectral Line Imaging.

Point defects play a crucial role in determining the properties of atomically thin semiconductors. This work demonstrates the controlled formation of different types of defects and their comprehensive optical characterization using hyperspectral line imaging (HSLI). Distinct optical responses are observed in monolayer semiconductors grown under different stoichiometries using metal-organic chemical vapor deposition. HSLI enables the simultaneous measurement of 400 spectra, allowing for statistical analysis of optical signatures at close to a centimeter scale. The study discovers that chalcogen-rich samples exhibit remarkable optical uniformity due to reduced precursor accumulation compared to the metal-rich case. The utilization of HSLI as a facile and reliable characterization tool pushes the boundaries of potential applications for atomically thin semiconductors in future devices.

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-Dependent Spectral Features.

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13C-labelled glucose. SIP-nanoFTIR simultaneously quantifies single-cell metabolism through infrared spectroscopy and acquires cellular morphological information via atomic force microscopy. The redshift of the amide I peak corresponds to the isotopic enrichment of newly synthesized proteins. These observations of single-cell translational activity are comparable to those of conventional methods, examining bulk cell numbers. Observing cells cultured under conditions of limited carbon, SIP- nanoFTIR is used to identify environmentally-induced changes in metabolic heterogeneity and cellular morphology. Individuals outcompeting their neighboring cells will likely play a disproportionately large role in shaping population dynamics during adverse conditions or environmental fluctuations. Additionally, SIP-nanoFTIR enables the spectroscopic differentiation of specific cellular growth phases. During cellular replication, subcellular isotope distribution becomes more homogenous, which is reflected in the spectroscopic features dependent on the extent of 13C-13C mode coupling or to specific isotopic symmetries within protein secondary structures. As SIP-nanoFTIR captures single-cell metabolism, environmentally-induced cellular processes, and subcellular isotope localization, this technique offers widespread applications across a variety of disciplines including microbial ecology, biophysics, biopharmaceuticals, medicinal science, and cancer research.

The Role of Luminescent Coupling in Monolithic Perovskite/Silicon Tandem Solar Cells.

Luminescent coupling (LC) is a key phenomenon in monolithic tandem solar cells. This study presents a nondestructive technique to quantitatively evaluate the LC effect, addressing a gap in the existing predictions made by optical modeling. The method involves measuring the ratio of photons emitted from the high bandgap top cell that escape through the rear, contributing additional current to the bottom cell, and to those escaping from the front side of top cell. The findings indicate that in the analyzed monolithic perovskite/silicon tandem solar cells, more than 85% of the emitted photons escaping from the perovskite top cell are used to generate additional current in the bottom cell. This process notably reduces the mismatch in the generated current between each subcell, particularly when the current is limited by the low bandgap subcell. The presented method is applicable to a variety of monolithic tandem structures, providing vital information for subcell characterization, providing vital information for predicting energy output and optimization for outdoor applications.

Viewing early life without labels: optical approaches for imaging the early embryo†.

Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.

Hyperspectral imaging reveals small-scale water gradients in apple leaves due to minimal cuticle perforation by Venturia inaequalis conidiophores.

Effects of Venturia inaequalis on water relations of apple leaves were studied under controlled conditions without limitation of water supply to elucidate their impact on the non-haustorial biotrophy of this pathogen. Leaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation. Reduced stomatal transpiration 3-4 d after inoculation coincided with a transient increase of water potential. Perforation of the plant cuticle by protruding conidiophores subsequently increased cuticular transpiration even before visible symptoms occurred. With sufficient water supply, cuticular transpiration remained at elevated levels for several weeks. Infections did not affect the leaf water content before scab lesions became visible. Only hyperspectral imaging was suitable to demonstrate that a decreased leaf water content was strictly limited to sites of emerging conidiophores and that cuticle porosity increased with sporulation. Microscopy confirmed marginal cuticle injury; although perforated, it tightly surrounded the base of conidiophores throughout sporulation and restricted water loss. The role of sustained redirection of water flow to the pathogen's hyphae in the subcuticular space above epidermal cells, to facilitate the acquisition and uptake of nutrients by V. inaequalis, is discussed.

Visualizing the structural and chemical heterogeneity of fruit and vegetables using advanced imaging techniques: fundamentals, instrumental aspects, applications and future perspectives.

Fruit and vegetables (F&V) are vastly complicated products with highly diverse chemical and structural characteristics. Advanced imaging techniques either combine imaging with spectral information or can provide excellent tissue penetration, and enable the possibility to target, visualize and even qualify the chemical and physical (structural) heterogeneity within F&V. In this review, visible and/or near infrared hyperspectral imaging, Fourier transform infrared microspectroscopic imaging, Raman imaging, X-ray and magnetic resonance imaging to reveal chemical and structural information in a spatial context of F&V at the macro- (entire products), meso- (tissues), and micro- (individual cells) scales are comprehensively summarized. In addition, their basic concepts and operational procedures, particularly sample preparation and instrumental parameter adjustments, are addressed. Finally, future challenges and perspectives of these techniques are put forward. These imaging techniques are powerful tools to assess the biochemical and structural heterogeneity of F&V. Cost reduction, sensor fusion and data sharing platforms are future trends. More emphasis on aspects of knowledge and extension at the level of academia and research, especially on how to select techniques, choose operational parameters and prepare samples, are important to overcome barriers for the wider adoption of these techniques to improve the evaluation of F&V quality.

Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention.

Novel melatonin-producing Bacillus safensis EH143 mitigates salt and cadmium stress in soybean.

Recently, microorganism and exogenous melatonin application has been recognized as an efficient biological tool for enhancing salt tolerance and heavy metal detoxification in agriculture crops. Thus, the goal of this study was to isolate and evaluate a novel melatonin-producing plant growth promoting bacterium. With high-throughput whole genome sequencing, phytohormone measurements, expression profiling, and biochemical analysis, we can identify a novel PGPB that produces melatonin and unravel how it promotes soybean growth and development and protects against salt and Cd stress. We identify the melatonin synthesis pathway (tryptophan→tryptamine→serotonin melatonin) of the halotolerant (NaCl > 800 mM) and heavy metal-resistant (Cd >3 mM) rhizobacterium Bacillus safensis EH143 and use it to treat soybean plants subjected to Cd and NaCl stresses. Results show that EH143 will highly bioaccumulate heavy metals and significantly improve P and Ca2+ uptake and the K+/Na+ (93%↑under salt stress) ratio while reducing Cd uptake (49% under Cd stress) in shoots. This activity was supported by the expression of the ion regulator HKT1, MYPB67, and the calcium sensors CDPK5 and CaMK1 which ultimately led to increased plant growth. EH143 significantly decreased ABA content in shoots by 13%, 20%, and 34% and increased SA biosynthesis in shoots by 14.8%, 31%, and 48.2% in control, salt, and Cd-treated plants, upregulating CYP707A1 and CYP707A2 and PAL1 and ICS, respectively. The melatonin content significantly decreased along with a reduced expression of ASMT3 following treatment with EH143; moreover, reduced expression of peroxidase (POD) and superoxide dismutase (SOD) by 134.5% and 39% under salt+Cd stress, respectively and increased level of total amino acids were observed. Whole-genome sequencing and annotation of EH143 revealed the presence of the melatonin precursor tryptophan synthase (trpA, trpB, trpS), metal and other ion regulators (Cd: cadA, potassium: KtrA and KtrB, phosphate: glpT, calcium: yloB, the sodium/glucose cotransporter: sgIT, and the magnesium transporter: mgtE), and enzyme activators (including the siderophore transport proteins yfiZ and yfhA, the SOD sodA, the catalase katA1, and the glutathione regulator KefG) that may be involved in programming the plant metabolic system. As a consequence, EH143 treatment significantly reduced the contents of lipid peroxidation (O2-, MDA, and H2O2) up to 69%, 46%, and 29% in plants under salt+Cd stress, respectively. These findings suggest that EH143 could be a potent biofertilizer to alleviate NaCl and Cd toxicity in crops and serve as an alternative substitute for exogenous melatonin application.

Measurements of Methane Emissions from a Biofertilizer Storage Tank Using Ground-Based Hyperspectral Imaging and Flux Chambers.

Open storages of organic material represent potentially large sources of the greenhouse gas methane (CH4), an emissions source that will likely become more common as a part of societal efforts toward sustainability. Hence, monitoring and minimizing CH4 emissions from such facilities are key, but effective assessment of emissions without disturbing the flux is challenging. We demonstrate the capacity of using a novel high-resolution hyperspectral camera to perform sensitive CH4 flux assessments at such facilities, using as a test case a biofertilizer storage tank for residual material from a biogas plant. The camera and simultaneous conventional flux chamber measurements showed emissions of 6.0 ± 1.3 and 13 ± 5.7 kg of CH4 h-1, respectively. The camera measurements covered the whole tank surface of 1104 m2, and the chamber results were extrapolated from measurements over 5 m2. This corresponds to 0.7-1.4% of the total CH4 production at the biogas plant (1330 N m3 h-1 corresponding to 950 kg h-1). The camera could assess the entire tank emission in minutes without disturbing normal operations at the plant and revealed additional unknown emissions from the inlet to the tank (17 g of CH4 h-1) and during the loading of the biofertilizer into trucks (3.1 kg of CH4 h-1 during loading events). This study illustrates the importance of adequate measurement capacity to map methane fluxes and to verify that methane emission mitigation efforts are effective. Given the high methane emissions observed, it is important to reduce methane emissions from open storage of organic material, for example by improved digestion in the biogas reactor, precooling of sludge before storage, or building gastight storage tanks with sealed covers. We conclude that hyperspectral, ground-based remote sensing is a promising approach for greenhouse gas monitoring and mitigation.

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.

Effects of endoluminal vacuum sponge therapy on the perfusion of gastric conduit in a porcine model for esophagectomy.

BACKGROUND: After esophagectomy, the postoperative rate of anastomotic leakage is up to 30% and is the main driver of postoperative morbidity. Contemporary management includes endoluminal vacuum sponge therapy (EndoVAC) with good success rates. Vacuum therapy improves tissue perfusion in superficial wounds, but this has not been shown for gastric conduits. This study aimed to assess gastric conduit perfusion with EndoVAC in a porcine model for esophagectomy. MATERIAL AND METHODS: A porcine model (n = 18) was used with gastric conduit formation and induction of ischemia at the cranial end of the gastric conduit with measurement of tissue perfusion over time. In three experimental groups EndoVAC therapy was then used in the gastric conduit (- 40, - 125, and - 200 mmHg). Changes in tissue perfusion and tissue edema were assessed using hyperspectral imaging. The study was approved by local authorities (Project License G-333/19, G-67/22). RESULTS: Induction of ischemia led to significant reduction of tissue oxygenation from 65.1 ± 2.5% to 44.7 ± 5.5% (p < 0.01). After EndoVAC therapy with - 125 mmHg a significant increase in tissue oxygenation to 61.9 ± 5.5% was seen after 60 min and stayed stable after 120 min (62.9 ± 9.4%, p < 0.01 vs tissue ischemia). A similar improvement was seen with EndoVAC therapy at - 200 mmHg. A nonsignificant increase in oxygenation levels was also seen after therapy with - 40 mmHg, from 46.3 ± 3.4% to 52.5 ± 4.3% and 53.9 ± 8.1% after 60 and 120 min respectively (p > 0.05). An increase in tissue edema was observed after 60 and 120 min of EndoVAC therapy with - 200 mmHg but not with - 40 and - 125 mmHg. CONCLUSIONS: EndoVAC therapy with a pressure of - 125 mmHg significantly increased tissue perfusion of ischemic gastric conduit. With better understanding of underlying physiology the optimal use of EndoVAC therapy can be determined including a possible preemptive use for gastric conduits with impaired arterial perfusion or venous congestion.

Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.

BACKGROUND: Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting. METHODS: Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized. RESULTS: A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%. CONCLUSION: To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.

Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study.

AIMS: To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes. MAIN METHODS: The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue. KEY FINDINGS: It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively. SIGNIFICANCE: This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.

Hyperspectral imaging-based erythema classification in atopic dermatitis.

BACKGROUND/PURPOSE: Among the characteristics that appear in the epidermis of the skin, erythema is primarily evaluated through qualitative scales, such as visual assessment (VA). However, VA is not ideal because it relies on the experience and skill of dermatologists. In this study, we propose a new evaluation method based on hyperspectral imaging (HSI) to improve the accuracy of erythema diagnosis in clinical settings and investigate the applicability of HSI to skin evaluation. METHODS: For this study, 23 subjects diagnosed with atopic dermatitis were recruited. The inside of the right arm is selected as the target area and photographed using a hyperspectral camera (HS). Subsequently, based on the erythema severity visually assessed by a dermatologist, the severity classification performance of the RGB and HS images is compared. RESULTS: Erythema severity is classified as high when using (i) all reflectances of the entire HSI band and (ii) a combination of color features (R of RGB, a* of CIEL*a*b*) and five selected bands through band selection. However, as the number of features increases, the amount of calculation increases and becomes inefficient; therefore, (ii), which uses only seven features, is considered to perform classification more efficiently than (i), which uses 150 features. CONCLUSION: In conclusion, we demonstrate that HSI can be applied to erythema severity classification, which can further increase the accuracy and reliability of diagnosis when combined with other features observed in erythema. Additionally, the scope of its application can be expanded to various studies related to skin pigmentation.

From consistent subjective assessment of skin sensitivity severity to its accurate objective scoring.

BACKGROUND: Sensitive skin (SenS) is a syndrome leading to unpleasant sensations with little visible signs. Grading its severity generally relies on questionnaires or subjective ratings. MATERIALS AND METHODS: The SenS status of 183 subjects was determined by trained assessors. Answers from a four-item questionnaire were converted into numerical scores, leading to a 0-15 SenS index that was asked twice or thrice. Parameters from hyperspectral images were used as input for a multi-layer perceptron (MLP) neural network to predict the four-item questionnaire score of subjects. The resulting model was used to evaluate the soothing effect of a cosmetic cream applied to one hemiface, comparing it to that of a placebo applied to the other hemiface. RESULTS: The four-item questionnaire score accurately predicts SenS assessors' classification (92.7%) while providing insight into SenS severity. Most subjects providing repeatable replies are non-SenS, but accepting some variability in answers enables identifying subjects with consistent replies encompassing a majority of SenS subjects. The MLP neural network model predicts the SenS score of subjects with consistent replies from full-face hyperspectral images (R2 Validation set  = 0.969). A similar quality is obtained with hemiface images. Comparing the effect of applying a soothing cosmetic to that of a placebo revealed that subjects with the highest instrumental index (> 5) show significant SenS improvement. CONCLUSION: A four-item questionnaire enables calculating a SenS index grading its severity. Objective evaluation using hyperspectral images with an MLP neural network accurately predicts SenS severity and its favourable evolution upon the application of a soothing cream.

Correlation analysis between texture features and elasticity of skin hyperspectral images in the near-infrared band.

BACKGROUND/PURPOSE: Skin elasticity was used to evaluate healthy and diseased skin. Correlation analysis between image texture characteristics and skin elasticity was performed to study the feasibility of assessing skin elasticity using a non-contact method. MATERIALS AND METHODS: Skin images in the near-infrared band were acquired using a hyperspectral camera, and skin elasticity was obtained using a skin elastimeter. Texture features of the mean, standard deviation, entropy, contrast, correlation, homogeneity, and energy were extracted from the acquired skin images, and a correlation analysis with skin elasticity was performed. RESULTS: The texture features, and skin elasticity of skin images in the near-infrared band had the highest correlation on the side of eye and under of arm, and the mean and correlation were features of texture suitable for distinguishing skin elasticity according to the body part. CONCLUSION: In this study, we performed elasticity and correlation analyses for various body parts using the texture characteristics of skin hyperspectral images in the near-infrared band, confirming a significant correlation in some body parts. It is expected that this will be used as a cornerstone of skin elasticity evaluation research using non-contact methods.

Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging.

BACKGROUND/PURPOSE: Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter-based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source- and target-domain datasets. METHODS: We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength-specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets. RESULTS: The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near-infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource. CONCLUSION: The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

Hyperspectral imaging for monitoring of free flaps of the oral cavity: A feasibility study.

OBJECTIVES: Hyperspectral imaging (HSI) provides spectral information about hemoglobin, water and oxygen supply and has thus great potential in perfusion monitoring. The aim of the present study was to investigate the feasibility of HSI in the postoperative monitoring of intraoral free flaps. METHODS: The 14 patients receiving reconstructive head and neck surgery with a radial forearm free flap were included. HSI was performed intraoperatively (t0), on Day 1 (t1), 2 (t2), 3-6 (t3), 7-9 (t4), 10-11 (t5) and 12-15 (t6) postoperatively. Flap tissue perfusion was assessed on defined regions of interest by calculating the perfusion indices Tissue Hemoglobin Index (THI), hemoglobin oxygenation (StO2 ), Near Infrared Perfusion Index (NIR Perfusion Index) and Tissue Water Index (TWI). RESULTS: Image quality varied depending on location of the flap and time of measurement. StO2 was >50 intraoperatively and >40 on t1 for all patients. A significant difference was found solely for TWI between t0 and t2 and t0 and t4. No flap loss occurred. CONCLUSIONS: The use of HSI in the monitoring of intraoral flaps is feasible and might become a valuable addition to the current clinical examination of free flaps.

Hyperspectral interference tomography of nacre.

Structural characterization of biologically formed materials is essential for understanding biological phenomena and their enviro-nment, and for generating new bio-inspired engineering concepts. For example, nacre-the inner lining of some mollusk shells-encodes local environmental conditions throughout its formation and has exceptional strength due to its nanoscale brick-and-mortar structure. This layered structure, comprising alternating transparent aragonite (CaCO3) tablets and thinner organic polymer layers, also results in stunning interference colors. Existing methods of structural characterization of nacre rely on some form of cross-sectional analysis, such as scanning or transmission electron microscopy or polarization-dependent imaging contrast (PIC) mapping. However, these techniques are destructive and too time- and resource-intensive to analyze large sample areas. Here, we present an all-optical, rapid, and nondestructive imaging technique-hyperspectral interference tomography (HIT)-to spatially map the structural parameters of nacre and other disordered layered materials. We combined hyperspectral imaging with optical-interference modeling to infer the mean tablet thickness and its disorder in nacre across entire mollusk shells from red and rainbow abalone (Haliotis rufescens and Haliotis iris) at various stages of development. We observed that in red abalone, unexpectedly, nacre tablet thickness decreases with age of the mollusk, despite roughly similar appearance of nacre at all ages and positions in the shell. Our rapid, inexpensive, and nondestructive method can be readily applied to in-field studies.

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection.

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.