This PDF file contains the front matter associated with SPIE Proceedings Volume 12770, including the Title Page, Copyright information, Table of Contents and Conference Committee lists.

Currently, intracranial pressure (ICP) is the primary clinical monitoring parameter for traumatic brain injury (TBI). However, during the hyperacute phase of TBI, ICP value lags behind changes of brain temperature (BT) and neurometabolic activity. This is due to the compensatory capacity of the intracranial space. Here, we explored BT changes and concentration changes of cytochrome c (∆Ccytc) during the hyper-acute phase of TBI. Nine mice were randomly divided into control group, mild TBI group, and severe TBI group. The mouse TBI model is created by using various levels of striking force. The strike device is designed and improved based on Feeney's free fall model. BT can be measured by a minimally invasive measurement probe, allowing for the observation of changes in both time and spatial distribution. ∆Ccytc was calculated using an intrinsic optical signal imaging system (IOSI). The relationship between BT and ∆Ccytc was analyzed during the development of mice TBI models.

Human upper gastrointestinal lesions include esophageal cancer, Barrett's esophagus, gastroesophageal reflux, etc. Through the diagnosis of upper gastrointestinal endoscopy, it is possible to capture images of symptoms and study relevant lesion in real time. The traditional inspection uses a tubular endoscope with a diameter of about 1 cm. It is inserted from the mouth, passes through the throat, esophagus, and finally reaches the stomach. During the inspection, the symptoms of the upper gastrointestinal are checked step by step. Due to the caliber of the pipeline equipment, it often causes discomfort at the throat. A new type of capsule endoscope with wires was born accordingly. It is mainly used to assist in the examination of symptoms in the upper gastrointestinal (esophagus, stomach, and duodenum). The patient can only swallow the capsule endoscope orally, just like swallowing capsule medicine. Follow-up doctors can control the posture of the capsule endoscope through the magnetic control handle to do examinations and obtain required images. However, compared with traditional endoscopes, capsule endoscopes cannot be equipped with additional narrow-band light sources, optical magnification and other functions on the endoscope due to the need for simplified volume, and the controllability of symptom observation is slightly insufficient. Based on this deficiency, this study deeply explores the narrow-band imaging technology that can be applied to capsule endoscopy. With the introduction of hyperspectral imaging technology, it reconstructs the capsule endoscopy image into spectral information, and combines the principle of color reproduction to optimize adjustment. Light source information, and finally reproduce the simulated narrowband image. Based on this narrow-band imaging technology, the narrow-band imaging function of the capsule endoscope can be endowed, which can help clinical examination to observe symptoms.

The current article introduces an innovative methodology for the categorization and identification of esophageal cancer through the utilization of hyperspectral imaging (HSI) and a deep learning model. The study focuses on identifying the cancer stage and marking its locations within the esophagus. A specialized algorithm has been employed in this study to generate simulated spectrum data from the hyperspectral imaging (HSI) data. This simulated data is then integrated with deep learning methodologies in a unified identification system based on the single-shot multibox detector (SSD). In order to assess the efficacy of the predictive model, dataset comprising of 155 white-light endoscopic (WLI) and 153 narrow-band endoscopic (NBI) images of esophageal cancer has been utilized. The algorithm effectively forecasts the outcomes of 308 examination images in a time frame of 19 seconds. The spectral data-based test results exhibit a precision of 88% for WLI esophageal cancer and 91% for NBI esophageal cancer images. Comparatively, the accuracy using RGB images is 83% for WLI and 86% for NBI. The present research exhibits noteworthy enhancement in prognostication precision, whereby the HSI detection approach elevates the precision of WLI and NBI by 5%. The findings of this research highlight the potential of combining HSI and deep learning for the detection and diagnosis of esophageal cancer. The proposed methodology offers enhanced precision in the identification of cancer stages and the precise demarcation of location. The outcomes of this study present encouraging prospects for advancement and utilization of HSI-derived techniques in the domain of esophageal cancer identification.

We experimentally demonstrate a simple, effective and intelligent scheme to obtain image monitoring of femtosecond laser processing of biological hard tissues. A simple mobile phone camera is adopted for imaging of different processing status of biological hard tissue. Subsequently, an automatic recognition method based deep learning is proposed to recognize the relationship between the optical images and manufacturing effect for the fast parameter optimization of laser processing. Correspondingly, the laser processing parameter could be well controlled to obtain qualified laser processing of biological hard tissue. These results indicate an efficient and accurate image monitoring route for intelligent femtosecond laser processing of biological

Optical coherence elastography (OCE) is an imaging technique for measuring elastic properties based on optical coherence tomography (OCT). Benefitting from the high-resolution imaging and sensitive motion detection of OCT, OCE has been used to assess the elasticity distribution of ocular tissue, skin tissue, etc. The biological tissues consist of multiple layers with various biomechanical properties and, thus, show directionally dependent elasticity. In previous OCE measurements, the tissues were regarded as isotropic materials, and the elasticity was measured in one direction, which may cause the bias of elasticity assessment. In this study, we developed the OCE method for directionally dependent elasticity assessment. Acoustic radiation force (ARF) generated by a ring ultrasonic transducer was applied to induce vibration in an axial direction and an elastic wave propagating in the lateral direction. The OCT beam, parallel to the ARF, scanned the sample through the central hole of the ring ultrasonic transducer. Then, phase-resolved OCT analysis was used to detect the vibration and visualize wave propagation. The vibration amplitude depends on the axial elasticity, and the lateral elasticity determines the elastic wave velocity. Therefore, the measurements of the vibration amplitude and the wave velocity using ARF-OCE can assess the elasticity of the sample in orthogonal directions. The results from the phantom measurements show that the OCE method can reliably evaluate the directionally dependent elasticity for the anisotropic samples.

Computer vision approaches have grown exponentially in recent years. Training AI models often requires annotated data. To increase effectiveness of this procedure one can use semi-automatic semantic annotation tools where some simplified approaches (based either on some pretrained models or visible features parameters) are implemented and manually tuned to isolate specific objects. OCT-signals contain information-bearing specific speckle structure and signal attenuation patterns. The parameters of these patterns corresponds to tangible tissue properties (such as scatterers spatial distributions), therefore can be used to construct semi-automatic semantic annotation tools. Using OCT-signal simulation approaches we evaluate the parameters of speckle patterns and attenuation coefficients and propose novel semantic annotation tools for OCT scans. We demonstrate the performance of semi-automatic 3D segmentation and annotation. This tool can be used as a supportive tool for AI applications as well as independent tool for semi-automatic scans segmentations and further characterization.

Viscosity measurement is critical in the fields of biomedicine and industry. Here, we propose a method based on optical coherence tomography (OCT) to quantitatively assess the Doppler viscosity of the liquid in microfluidic devices. The velocity of the liquid in a silicone tube was obtained by Doppler optical coherence tomography (DOCT), by analysing the phase change between sequential B-Scan. Two manometers were used to measure the real-time pressure difference between the inlet and outlet of the silicone tube. Finally, the viscosity of the flow sample was calculated according to Poiseuille’s law. Different viscous liquids and blood samples were tested, and the results were consistent with data reported in the literature. Experimental results indicated that the proposed method can be a new tool for non-contact and fast liquid viscosity measurement and even be engineered in the future for daily monitoring of blood viscosity with only a small amount of blood.

This paper introduces an adaptive method that significantly improves visualization quality of reconstructed strains in phase-sensitive optical coherence elastography (OCE). The strain is estimated by finding axial gradients of interframe phase variations and the phase-variation gradients are estimated over a certain scale. The noise level of the so-found strain maps heavily depends on the chosen scale that is used for gradient estimation. Choosing a scale that is too small or too large can degrade the results of elastographic visualization. In real conditions the spatial strain distribution usually is essentially inhomogeneous. Obtaining the best results in different areas of OCT scans requires utilization of different scales, although usually some fixed “compromise” scale over the entire OCT image is used to estimate phase-variation gradients. To improve the quality of strain maps in phase-sensitive OCE, we propose a method of automatic adaptive selection of this scale depending on the level of local strain in the visualized area. The proposed method is elucidated using on both numerically simulated and real OCT scans which are characterized by significant spatial inhomogeneity of strains.

The invasion and metastasis of tumor cells are not only related to the tumor cells themselves, but also closely intertwined with other components of the tumor microenvironment. The connective cells, including fibroblasts and myofibroblasts, play a crucial role in determining the remodeling of collagen fibers in the tumor microenvironment during the process of tumor invasion, which determinant of tumor migration. In this study, we used the multi-photon imaging system to identify two different prognostic tumor-associated collagen signatures (TACS4 and TACS6). Then, the multi-photon images were used to co-locate with HE images, and open-source convolutional neural network Hover-Net was used to segment, classify and quantifythe nuclear feature. Our results showed that the connective cells are opposed to tumor cells and do not mix with tumor cells in TACS4, while in TACS6, they are scattered promiscuously with tumor cells. The spatial distribution of connected cells in TACS4 is denser than that in TACS6. These differences in spatial distribution of connected cells may potentially have different prognostic information.

Liver fibrosis is a response to chronic liver damage, causing the accumulation of extracellular components like collagen fibers. Accurate evaluation of fibrosis is key for predicting disease prognosis. Multiphoton microscopy (MPM) is an advanced technique that allows label-free imaging of biomedical tissues using femtosecond laser-induced nonlinear optical effects. In this study, hepatic tissue samples were imaged via MPM and then imaging data were analyzed with Hover-Net, a convolutional neural network. We find that MPM has the ability to directly observe fibrotic changes, and also find that the number of portal bile ducts are positively related to liver fibrosis and can be automatically identified by the Hover-Net. These findings suggest MPM combining with deep learning can assess liver fibrosis quickly and reliably without the need for exogenous contrast agents.

Ductal carcinoma in situ (DCIS) accounts for approximately 20% of all breast cancer. DCIS is a form of breast cancer that is restricted to the ducts and has not invaded surrounding breast tissue or spread to lymph nodes or other parts of the body. The grades of DCIS are classified as low, intermediate, and high, based on cytonuclear features, and high-grade DCIS has a higher risk of progressing into invasive ductal carcinoma (IDC). The collagen fibers are an important component of the tumor microenvironment (TME) in DCIS and play an important role in tumor formation and progression. Multiphoton microscopy (MPM) based on second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) can monitor the morphological changes of collagen fibers around DCIS. SHG is currently considered the gold standard for visualizing collagen fibers and has been widely employed in various cancer-related studies of collagen fibers. Our investigation employed MPM imaging of breast tissue to observe the differences in collagen fibers within three distinct grades of DCIS. Through image processing, we were able to quantify various attributes of collagen fibers enveloping DCIS lesions of varying grades. The study found that collagen fibers surrounding low-grade DCIS were denser and exhibited more sinuous shapes, whereas collagen fibers around intermediate and high-grade DCIS lesions were less dense and exhibited a more organized arrangement. The study suggests that MPM imaging is a powerful tool for investigating the microenvironment of DCIS and may provide valuable information for predicting disease progression and prognosis.

The current study proposes a lossy mode resonance (LMR) sensor for ascorbic acid detection. LMR sensors are significant in the field of biosensing because of their high sensitivity, specificity, and versatility. The detection of proteins in blood or other biological fluids, DNA hybridization in genetic testing, and bacteria or viruses in environmental or clinical samples are all applications of the use of LMR sensors in biosensing. With the ability to detect biomolecules with enhanced performance, the use of nanomaterials in biosensing has the potential to revolutionize the area. It can have substantial effects on identifying diseases, developing new drugs, and environmental monitoring. With graphene oxide (GO), a novel SMS (single mode fiber-multi mode fiber-single mode fiber) structure has been developed to detect ascorbic acid. GO nanoparticles have been used in this work over an etched optical fiber for higher sensitivity. Experimental testing is done to determine the sensor's effectiveness in the detection of the analyte.

We developed a time domain near infrared optical tomography (TD NIROT) system for tissue imaging. To facilitate its evaluation, we created a dynamic tissue-mimicking phantom platform characterized by fluctuating internal optical properties and arbitrary anomaly shapes. Using water-soluble materials, we 3D printed and embedded shapes within silicone mixtures, and created the corresponding internal hollow structure after curing and submerging the phantom in water. By introducing liquid solutions of varying dye concentrations, we controlled the optical properties of the internal shape over time. The phantom was measured with our TD NIROT system. The internal shape was accurately imaged and the local dynamic changes introduced by the pumping system was revealed . These results show a great potential of such phantom systems for applications in assessing various optical imaging systems.

Functional near infrared spectroscopy (fNIRS) is a non-invasive and safe method to measure hemodynamics of the cerebral cortex. The use fNIRS in combination with brain radiotherapy is currently of our high interest since it provides a method to measure tissue oxygenation levels during irradiation. This can potentially help to advance the field of personalized cancer treatment. However, one limitation of the fNIRS technique is associated with penetration depth into tissues. Tissue optical clearing (TOC) could potentially improve the efficiency of fNIRS and for this use of radiopaque solutions, such as Omnipaque™ (iohexol), Visipaque™ (iodixanol), etc., are proposed as effective optical clearing agents (OCA). The presented study experiments the feasibility of using TOC for fNIRS measurements in radiotherapy. The effectiveness of isosmotic Visipaque™ and an OCA with a high osmolarity, a solution of 70% glycerol with 10% DMSO, was evaluated when using chicken samples.

The multiwavelength focused ultrasound (FUS)-optic technique in development aims to provide application for spatially accurate measurements of tissue properties in the focus area in real-time. But setting up such a system is quite challenging due to several factors, for instance the integration of different optical sources, positioning of the optical and acoustic beams on the phantom, and synchronization. In this presentation, we present first steps towards designing a multiwavelength FUS-optic system that measures wavelength dependent changes in the focus area. As a test substance, we used intralipid solution, milk, and the mixture with different levels of concentration. The expected signal peak heights of tagged photons intensities of different wavelengths are partly dependent on the concentration due to the wavelength-dependent optical absorbance. Furthermore, detected FUS modulated amplitudes are in relation with the concentration of sample in focus area. The aim of this study is to characterize the relationship between FUS-optic signal peak heights and the different levels of sample concentration at different wavelengths.

Diabetic foot ulcers are a significant complication that afflicts diabetes patients, posing a serious threat to their quality of life and health. However, challenges persist in the current landscape of risk assessment and treatment prognosis regarding diabetic foot ulcers. Hyperspectral imaging, an advanced non-invasive detection technology, has garnered considerable attention for its potential in assessing and predicting the development risk and non-invasive healing possibilities of diabetic foot ulcers. Given the challenges in collecting hyperspectral datasets, this paper visually reconstructs hyperspectral images from the available optical data for diabetic foot ulcers. It visualizes the oxygen saturation in the ulcer region and concludes that the average oxygen saturation for ulcers that eventually healed falls within the range of 54% to 64%, while for ulcers that did not heal, the average oxygen saturation ranges from 32% to 48%.

In this study, a novel optical fiber sensor based on varying cladding diameters is developed to detect different tyramine concentrations. Single-mode fiber (SMF, 8.25/125 μm) and multimode fiber (MMF, 200/220 μm) are fused to form the SMF-MMF-SMF structure. The output signal is amplified by etching the fiber sensing region that can generate stronger evanescent fields. Multi-walled carbon nanotubes (MWCNTs) are used to enhance the sensing performance. The tyramine enzyme is used to functionalize the probe and realize the specificity of the probe for tyramine. The performance of the sensor is analyzed, and results show that the sensor has good selectivity. The results also show that the sensors based on different cladding diameters can be used as special sensors for measuring the concentration of tyramine.

Angiogenesis is essential for bone homeostasis and repair. Newly formed vessels convey osteogenic progenitors during bone regeneration. However, the lack of continuous and label-free visualization of the bone microvasculature has resulted in little understanding of the neovascular dynamics. Here, we take advantage of optical-resolution photoacoustic microscopy (ORPAM) for label-free, intravital, long-term observation of the bone vascular dynamics. We quantify the angiogenic effect of locally-applied vascular endothelial growth factor (VEGF) in the mouse tibia defect model. VEGF treatment increased the concentration of total hemoglobin, vascular branching, and vascular density, which increased bone formation within the defect. These data demonstrated ORPAM as a useful imaging tool understand bone angiogenesis, and revealed the effectiveness of locally delivered therapeutic agents with sufficient sensitivity, contributing to the studying the bone regeneration in the future.

Nucleic acid detection techniques have played a crucial role in identifying specific genetic indicators or species, with Polymerase Chain Reaction (PCR) being the established gold standard in this field. However, PCR's dependence on specialized equipment and skilled personnel has limited its utility in resource-limited or field settings, and its multitemperature stage protocol hinders rapid nucleic acid detection. The emergence of isothermal amplification methods, particularly recombinase polymerase amplification (RPA), has addressed some of these limitations, offering high sensitivity and efficiency. Nevertheless, the challenge of RPA amplicon detection, typically reliant on labeling methods, has persisted, potentially introducing false positives and increased costs. This study introduces an innovative approach to nucleic acid detection, harnessing hyperspectral quantitative interference for label-free, isothermal nucleic acid detection within a remarkably short 25-minute timeframe. By employing a solid-phase RPA amplification process that transforms the product into a DNA molecule layer and leveraging Fourier domain optical slice separation and spectral phase shift analysis, this method enables the semi-quantitative determination of amplification results. The integration of digital microfluidic technology further enhances the method's performance, enabling parallel, integrated, and clinical multi-indicator pathogen detection. Overall, this research presents a practical and rapid solution for label-free nucleic acid detection, addressing the current limitations associated with nucleic acid amplification techniques. This advancement holds promise for a wide range of applications, from point-of-care diagnostics to field-based pathogen detection, ultimately contributing to more accessible and efficient nucleic acid testing methodologies.

We present our new microscope prototype, which uses the single-pixel imaging method to image fluorescent samples. Specifically, our microscope generates sinusoidal patterns of excitation light by interfering two laser beams ‘steered’ individually by optical deflectors. We imprint a series of such sinusoidal patterns onto the sample and use a single-pixel detector to acquire a signal-readout for each pattern. Ultimately, this allows us to ‘build’ an image of the sample. Our application of optical deflectors opens the possibility to implement a microscope that provides sharp images fast while optimizing the fluorescence duty cycle, making it suitable for long-term observation of organ-on-chip systems.

Multi-species blood identification is especially useful in animal quarantine, import and export, criminal cases, forensic examination, and wildlife conservation. Raman spectroscopy is a non-destructive, label-free, and highly specific method for providing chemical information on materials and serving as an analytical tool to characterize biological samples. Machine learning approaches in conjunction with Raman tweezers are proposed to extract the Raman spectral characteristics of single red blood cells (RBCs), then the cells based on the spectral characteristics are classified, and some classification prediction models to achieve single cell identification of different blood species are achieved finally.

The authors concluded that high temperature microwaves cause damage to the germinal membrane, which affects germination activity, and this process is exacerbated with increasing temperature. High temperatures stimulate germination channels to promote germination, but leakage of DNA and key proteins inhibits subsequent growth, resulting in a significant decrease in growth rate and a slight increase in germination rate under higher temperature microwave treatment. In addition, the surface of the spores appeared wrinkled after high-temperature microwave radiation and became smaller with increasing temperature, most likely due to the leakage of molecules such as CaDPA.

Platelet aggregation rate is a crucial indicator for platelet function testing in clinical practice. Currently, the most common method for detecting platelet aggregation rate is by using a platelet aggregation analyzer to detect platelet-rich plasma or washed platelets. This method works based on the principle of light transmittance ratio, which measures the difference of the light transmittance before and after the reaction between the sample and the inducer to represent the aggregation rate of the sample. Although this method only requires a few minutes to complete after the sample is collected and processed, it requires a large sample volume. Moreover, it cannot accurately detect samples with low aggregation rate and chyle blood due to the slight difference in light transmittance before and after the reaction, which poses significant challenges for doctors in the clinical diagnosis and treatment determination for platelet aggregation dysfunction. To overcome this limitation, we propose a novel method for assessing platelet aggregation rate by leveraging the capabilities of digital microfluidics for automated precision manipulation of minute liquid droplets.

IR780 is widely used as a commercially available near infrared contrast agent due to superior optical properties and tumor targeting capacity, while is a photosensitizer for photothermal (PTT) and photodynamic therapy (PDT). However, few articles reported the accumulation, dynamics and retention of IR780 in tumor. In the work, we explored the accumulation and retention of IR780 in tumor by quantitatively recovering the three-dimensional distributions of IR780 using a home-made diffuse fluorescence tomography (DFT) prototype system. The results showed that DFT can effectively identified multiple tumor targets with a significant tumor-to-normal-tissue ratio simultaneously. The kinetics of fluorescent signals in tumors and liver showed that IR780 was quickly cleared from the liver, whereas exhibited much higher tumor retention over 7days. Ex vivo imaging of dissected organs and fluorescence signal analysis revealed that IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs, suggesting that IR780 had excellent tumor homing ability.

Light scattering imaging is a promising label-free technique for its sensitivity to the size and structure of both homogeneous and heterogeneous samples. This study explores the refractive index sensitivity of light scattering imaging to differentiate polystyrene and silica microparticles. Here, we use a light scattering imaging system to get the side scattering images of single microparticles. The obtained images are in good agreement with Mie theory simulation results. We demonstrate that light scattering could provide a high-sensitive image with a 0.2 difference in the refractive index within a small angular range (79º - 101º). This sensitivity is further validated by analyzing image datasets of polystyrene and silica microparticles based on the scattered far-field components at a positive defocusing distance. Our findings reveal that the ratio between the intensity and prominence of the side lobes is a potential marker for refractive index differences of the microparticles. There is a significant difference between each of the two types of polystyrene (3.87 μm and 4.19 μm, mean diameters) and the silica microparticles (3.93 μm, mean diameter) in the scattered field properties. This capability of light scattering imaging for refractive index differentiation could provide insights into the internal structure of heterogeneous samples, such as label-free single cells.

Light scattering techniques provide a broad range of label-free measurements on the submicron and nanoscale levels. Various noises influence nanoparticle light scattering images, affecting image information. This work investigates the noise effect on light scattering imaging-based classification of label-free nanoparticles. Here, we developed a deep learning model based on AlexNet to study the effect of noise on light scattering images for nanoparticle classification. A fiber-based light scattering imaging system is used to detect nanoparticles of 120 nm, 65 nm, and 41 nm in diameter. Three noise models of blurring, Gaussian, and speckle noise are modeled to mimic the light scattering noise. The trained model is tested separately using the original and artificial noisy images to estimate the effect of the noise on the classification performance. The results show that deep learning can distinguish between nanoparticle images of various sizes with an accuracy of 91.5 %, while the three types of noise affect the classification performance. The speckle noise shows the highest effect, with an accuracy drop of around 10%, followed by the Gaussian noise and blurring. With the increased demand for label-free nanoparticle imaging, it is interesting to determine the noise model and preprocessing steps for optimized classification.

X-ray digital radiography and fluorescence dual-modality imaging system has become an important tool for in-vivo biomedical researches. However, current dual-modality imaging system generally employs two independent pixelated detectors, which is costly and bulky. Here we propose a novel approach to achieve dual-modality imaging system utilizing only one single-pixel detector. The prototype of the system is built based on a Fourier single-pixel imaging architecture. The spatial resolution of the X-ray digital radiography modality was measured to be 1.81mm, the sensitivity and the imaging depth of the fluorescence imaging modality was evaluated to be 1.48 nmol/ml and 4 mm, respectively. Compared with the conventional system, our system is cost affordable, with a more compact structure, and free of image registration from different modalities. In-vivo imaging results of a C57BL/6 female mouse bearing tumor targeted with mCherry demonstrates its capability for small animal researches.

Continuous blood pressure (BP) estimation helps with the hypertension treatment. However, continuous BP estimation is only possible with an invasive catheter measurement method as the gold standard. In this study, we proposed an Attention-U-Net neural network for noninvasive continuous BP estimation using photoplethysmography (PPG) signals. Specifically, the proposed Attention U-Net architecture was evaluated on Physionet’s Cuff-Less Blood Pressure Estimation Dataset. A self-attention mechanism is added in front of each decoder in U-Net, and a feature extraction-recovery layer is added after the last decoder. The experimental results validate the feasibility of the proposed method for continuous BP estimation based on PPG. Meanwhile, the results also show that Attention-U-Net has better performance than Res-Net, Dense-Net, GRU based Seq2Seq and etc. Therefore, the proposed method is a promising alternative for noninvasive continuous BP estimation.

In polarization imaging, circular and linear polarization are conventionally used for incident light detection. However, relying solely on these modes may lead to a loss of crucial polarization data. This study expands the incident polarization state from a single point on the Poincaré sphere to its entire surface, yielding the Global-Polarization Stokes Ellipsoids (GPSE). GPSE offers an intuitive representation of polarization response discrepancies. It reveals distinct characteristics when birefringence and scattering effects act independently and analyzes their coupled effects in concurrent operation. In systems with both birefringence and scattering, GPSE parameters provide a robust characterization of anisotropy, overcoming issues for linear phase retardation, like phase wrapping and helicity flip associated with circular polarization. This approach provides a novel perspective and methodology for polarization imaging and information extraction.

Structured light 3D surface imaging reconstructs the 3D surface shape of an object by analyzing the deformation of a projected structured-light pattern. The detection of the pattern is a crucial step in this technique, which can be affected by the blurring of the patterns due to tissue scattering and absorbing. In this study we experimentally investigate how the projected structured light based on hyperspectral projection. A hyperspectral projector is constructed to generate patterns of different wavelengths, bandwidth and densities. The projected structured light is tested on porcine tissue in vitro. The sharpness of all the patterns and property of tissue are evaluated. The results indicate that tissue-dependent wavelength of light with appropriate density of pattern yield optimal illumination which corresponds to 3D reconstruction accuracy for structured light 3D endoscopy.

Optical coherence tomography (OCT) enables high-resolution, label-free two-dimensional cross-sectional and three-dimensional volumetric imaging of biological tissues. Combining OCT imaging with external force excitation, optical coherence elastography (OCE) provides noninvasive elasticity quantification of samples for the pathological analysis of tissues and early diagnosis of diseases. However, the OCE system with a fixed OCT sample arm cannot be used for elasticity measurements of tissues located in a narrow space, such as an oral cavity and an ear canal, because the OCT beam and external force cannot easily reach the tissues. In this study, we developed a handheld OCE method for the elasticity measurements based on elastic wave imaging. The handheld probe integrated an air pulse excitation unit and a microelectromechanical system-based scan imaging unit. A short air pulse induced the elastic wave in a sample. Then the OCT data was captured by an M-B scan protocol, and the tissue vibration was analyzed by Doppler phase shifts. After elastic wave visualization, the elastic wave velocity was calculated for the elasticity quantification of the sample. The results show that the handheld OCE method can induce and image the elastic wave and, thus, quantify the elastic modulus with high flexibility for the tissue in a narrow, deep space.

Optical coherence tomography (OCT) is a non-invasive, label-free imaging technique with high resolution. Due to the relatively low scan rate of OCT and involuntary bulk motion of tissues, the OCT image will be distorted by the motion artifacts. The motion artifacts can be reduced by hardware and software methods. In hardware methods, additional hardware is used to track the motion of the object, and extra scans may be required during data acquisition. The software methods can simplify the device and the data acquisition. However, the motion correction based on the cross-correlation analysis is time-consuming. In this study, we proposed a fast motion correction method for OCT images based on image feature matching. First, the motion-related mismatch in the slow scan direction was compensated by the image feature matching between the adjacent B-scans based on the oriented FAST and rotated BRIEF (ORB) approach. Then the axial motion in A-scans was corrected by the boundary detection of the tissue structure and the non-rigid transformation between the corresponding A-lines in the adjacent B-scans. The fast motion correction method was validated by the OCT imaging of a rat ear. The results show that the method can effectively correct motion artifacts of OCT images with a fast processing speed.

Optical coherence tomography angiography (OCTA) has emerged as a highly competitive technique for visualizing blood perfusion without the need for exogenous contrast agents. However, for high-resolution optical imaging, a tight optical focus is usually needed to achieve the diffraction-limited resolution in optical microscopy, which results in a limited depth of field (DOF), making it challenging to obtain large-scale, non-stitched, high-resolution images of samples with uneven surfaces without performing Z-axis scanning. To solve this problem, we introduce the diffractive optical elements (DOEs) into the conventional Gaussian beam (GB) OCT system and develop a needle-shaped beam (NB) OCT system with both long DOF and high resolution, which maintains 8 μm lateral resolution over a depth range of 620 μm, allowing real-time, non-stitched, large-field imaging of samples with uneven surfaces. OCTA imaging of mouse brains with natural curvature was demonstrated in our work.

Multiple myeloma may develop resistance to certain drugs during chemotherapy, which have a fatal impact on treatment efficacy. At present, the drug resistance detection methods for multiple myeloma, such as proteomic identification and clone formation analysis, are relatively complex, and the accuracy and detection time are not ideal. In our work, laser tweezers Raman spectroscopy was used to collect 412 groups of spectra of two kinds of cells, namely, MM.1R and MM.1S, which were respectively resistant to dexamethasone and sensitive to dexamethasone. We selected support vector machine, random forest, linear discriminant analysis and other algorithms to train the pretreated Raman spectra, and the recognition accuracy on the test set was above 95%. This result shows that the combination of laser tweezers Raman spectroscopy and artificial intelligence algorithm can quickly detect drug resistance of cancer cells.

Current Hyperspectral stimulated Raman scattering (hsSRS) data analysis methods face challenges when it comes to rapidly and reliably quantifying different lipid subtypes, and cannot fully leverage the information in hsSRS data. Here, we present a rapid and reliable quantitative algorithm for quantitative analysis that fully extracts chemical information by using adaptive selection of Lorentzian basis functions to fit the spectra in hsSRS data in bulk. We demonstrated that, by utilizing the ratio relationships between fitted bands, quantitative comparisons of specific lipid subtypes can be achieved. Moreover, we applied our method for the quantitative analysis of lipid composition in lipid droplets based on hsSRS data of liver cancer tissues and confirmed our method has a better fitting effect and a faster solving speed compared to MCR. This suggests that our method has the potential for great utility in the quantitative analysis of hsSRS imaging data for biomedical specimens.

Nowadays the problem of creating high-quality video recordings and video images of animal behavior is extremely relevant for tracking purposes. Minimal optical distortion is required for a correct operation of the tracking system and further analysis of the experiments. One of the effective methods of quality increasing is providing the laboratory installation with a powerful light source. This research presents a possibility of implementation of a laser diode lighting sources in experiments of rodent tracking.

Lung cancer is one of the most prevalent cancers in China and has the highest mortality rate. Early-stage lung cancer often presents as lung nodules, which lack specific clinical features and can be easily overlooked, resulting in late-stage diagnoses. Unfortunately, there is a lack of effective techniques for early detection of lung cancer. Surface-enhanced Raman spectroscopy (SERS) offers a fast, simple and non-invasive qualitative or quantitative analysis. Additionally, the weak Raman scattering signal of water makes SERS an ideal tool for studying biological samples in aqueous solutions. The objective of this study was to evaluate the feasibility of a SERS-based label-free nano-biosensor for distinguishing lung cancer patients from healthy volunteers. Herein, silver nanoparticles were directly mixed with human serum as SERS active nanostructures to enhance the Raman scattering signal, enabling high quality SERS spectra to be obtained from 50 lung cancer patients and 50 healthy volunteers. The results showed that the SERS spectral properties (spectral intensity) in the serum of lung cancer patients were significantly different from those of normal subjects due to biomolecular changes. High classification accuracy can be achieved using PCA-LDA diagnostic algorithms and machine learning techniques. This exploratory study demonstrates the great potential of the serum SERS method as a fast and convenient tool for lung cancer diagnosis and screening

Blood vessels are not only the channel of tumor growth and metastasis, but also the channel of tumor drug therapy. With the development of laser technology, laser treatment of tumor has attracted more and more attention. The changes of blood vessels and tumor parenchyma during photothermal therapy directly reflect the effect of photothermal therapy on tumor. In this paper, speckle variance OCT was used to monitor the dynamic changes of blood vessels and tumor parenchyma in laser photothermal therapy. Specifically, taking the mouse ear as an animal model, the spectral domain OCT combined with speckle variance algorithm was used to monitor the changes of blood vessels and tissues in real time during tissue 830nm continuous laser irradiation. At the same time, the infrared thermal imager is used to monitor the temperature change in the process. To establish the correlation between temperature and the damage of tissue and blood vessels under laser irradiation. The results will provide an effective evaluation method for photothermal therapy and an auxiliary method for tumor treatment monitoring

Laser speckle contrast imaging (LSCI) is a non-scanning full-field hemodynamic imaging technology, which has the advantages of real-time, non-invasive, and high spatiotemporal resolution. It has become a widely used optical imaging technology for vascular visualization and dynamic blood flow monitoring. The Reflect-LSCI (R-LSCI) system is mostly used for the imaging of superficial blood vessels, exhibiting poor image quality when it comes to deep vascular visualization. Some studies have shown that Transmissive-LSCI (T-LSCI) has advantages in deep tissue imaging. At the same time, adaptive window space direction (awsdK) method has better imaging quality for deep blood vessels in R-LSCI. In this study, we used several LSCI methods processed in the spatial domain to compare the speckle images acquired by the R-LSCI system and the T-LSCI system. The results of comparative experiments show that in the T-LSCI, the awsdK also has the ability to improve the visualization of deep blood vessels without changing the relative velocity information. At the same time, the reflection speckle images and transmission speckle images were compared. The results showed that the T-LSCI was better than the R-LSCI in deep tissue imaging for a certain thickness of tissue

Achilles tendon rupture has become an important problem with the increasing incidence in recent years. At the same time, there are many methods for the treatment of Achilles tendon rupture. How to statistically assess the severity of Achilles tendon rupture is significant for judging the effectiveness of the treatment methods. There are many methods to evaluate the severity of Achilles tendon rupture in clinical practice, most of which are invasive or require staining, which is relatively cumbersome. Because of its sensitivity to microstructures, particularly anisotropy analysis in disease diagnosis, Mueller matrix polarimetry has shown promise as a label-free technique in biomedical research. Therefore, Mueller matrix polarimetry is used to evaluate the severity of the Achilles tendon rupture. The results show that Muller matrix parameters could distinguish the normal Achilles tendon samples from the injured Achilles tendon samples and could distinguish trends in the internal composition of the Achilles tendon. We provide a non-contact and non-labeling in situ method for the assessment of Achilles tendon rupture, which is very promising.

Multispectral endoscopic imaging is a promising technique for lesion detection, and surgical guidance. Based on different spectral properties of tissues, multispectral imaging can provide enhanced contrast of vascular structures (narrow band imaging) or enable the quantitative analysis of hemoglobin and lipid. The multiparameter phantoms serve as efficient tools for system calibration, performance evaluation, and algorithm development in multispectral endoscopy are needed. In this paper, we developed a multiparameter tissue-mimicking phantom that mimics the parameters of human gastric mucosa, such as scattering coefficient, scattering layer thickness, vascular width, lipid on the surface and blood oxygen saturation (SO2). We verified the SO2 measurement accuracy by comparing with commercial i-STAT devices for SO2 distribution imaging. At the same time, the segmentation of lipid regions was also tested. Our results demonstrate that this multiparameter phantom is a versatile tool that can facilitate validation and evaluation of multispectral endoscopic systems.

Optical coherence tomography (OCT) is a non-invasive 3D imaging technique that provides high-resolution images and has been extensively used in biomedical research and clinical studies. Although micrometer resolution is already considered high for biological tissue imaging, the need for even higher resolution remains constant. Improving the resolution of OCT images can reveal previously unseen microstructures, which can aid in achieving more accurate diagnoses. Currently, the resolution of OCT images is primarily constrained by speckle noise and spectral bandwidth limitations. We have achieved simultaneous suppression of speckle noise and resolution improvement in OCT images in our previous work. However, traditional methods based on prior optimization iteration have a high computational cost, which limits its applicability. In this paper, we propose an improved deep learning model called DRUNET (Dilated Residual U-Net) to achieve noise reduction and resolution improvement simultaneously. The model incorporates dilated convolution and residual learning to enhance the learning capacity of the U-Net. In addition, we apply a simple, yet effective attention module called Convolutional Block Attention Module (CBAM) to improve DRUNET performance. We evaluate the performance of the DRUNET model in denoising and improving resolution on two types of OCT images. The experimental results demonstrate the effectiveness of the proposed model, which enables us to batch process poor-quality OCT images quickly without requiring any parameter fine-tuning under time constraints.

In endoscopic optical coherence tomography (EOCT), compact optical structure is often required for the fiber probe to access lumen tissues through a tortuous path. However, to achieve this, current endoscopic probes often have short working distance and complex fabrication technique. In this study, we report two fiber probes, which can be applied in long working distance endoscopic imaging. The first one is a ball lens probe, which consists of a beam expansion part composed of coreless optical fibers and a focusing and reflecting part composed of half ball lens. This method is a simple and low-cost probe fabrication technique that utilizes the surface tension of the molten material itself to form the ball lens on the fiber tip and creates reflective surface by sanding. The other one is a 3D micro printing probe, which consists of a coreless optical fiber part for beam expansion and a focusing and reflecting part composed of freeform micro-optics easily created by 3D micro printing technology. The freeform micro-optics created by this method can compensate for the influence of the catheter on light focusing, thereby enhancing imaging quality. We apply these two probes into a customized SD-OCT system to test the imaging on infrared cards, tapes, and various biological tissue samples. Both probes achieve imaging at a working distance of 6mm. In the future, the compact design, cost-effective and long working distance of our fiber probe will enable broader applications in more endoscopic catheter applications.

Pathological changes in ocular tissues can cause differences in tissue elasticity. Therefore, tissue elasticity is a valuable indicator of early ocular diseases. To measure the elasticity of ocular tissues, we proposed a whole-eye optical coherence elastography (OCE) method. To image the whole eye, an electrically tunable lens (ETL) was integrated into the system to adjust the focal plane. For inducing elastic waves in an entire eye, the acoustic radiation force (ARF) from one transducer stimulated the anterior segment from the side of the eye, and another transducer excited the retina from the front of the eye. The elastic waves in the porcine cornea and retina were visualized by Doppler phase shift analysis. Then, we measured the elastic wave propagation velocities and calculated Young's moduli of the tissues. In the ex-vivo experiments of porcine eyes, the elastic moduli of the cornea and retina were quantified, respectively. The experimental results demonstrate the feasibility and potential clinical value of the proposed method for assessing tissue elasticity in ocular structures.

Hypoxia in malignant tumors can have an inhibitory effect on photodynamic therapy, chemotherapy, radiotherapy, immunotherapy, and chemodynamic therapy. In previous studies, nanoparticle platforms combined with drugs or direct oxygen-carrying to increase oxygen content are usually used. Here, we designed to improve tumor hypoxia by injecting human-derived hemoglobin-based oxygen carriers (h-HBOC) synthesized from expired human blood or human placental blood, and real-time quantitative oxygen concentration monitoring was achieved with the help of photoacoustic mesofunctional imaging, and the oxygen concentration was calculated using a multispectral unmixing method. Firstly, we combined photoacoustic mesoscopy to monitor the blood oxygen concentration of the same batch of mice injected with different concentrations of h-HBOC, and verified the positive correlation between the h-HBOC concentration and the blood oxygen level without affecting the hemoglobin of the animals themselves. The relative optimal dose of h- HBOC for targeting 4T1 tumor line mice was determined to be 600 mg/kg, with the sixth hour after h-HBOC injection being the relative optimal moment of treatment. A method for determining the optimal moment of tumor microenvironment by real-time monitoring of blood oxygenation in malignant tumors has been developed to determine the optimal time of treatment for therapeutic means. This therapeutic strategy can formulate differentiated and visualized therapeutic strategies for individual individuals according to different tumor lineages and bio-individuals, bridging the individual differences and obtaining the optimal therapeutic plan. Since the improvement of tumor microenvironment by h-HBOC is sustained in the long term and can be used to improve photodynamic therapy, and chemotherapy and radiotherapy have positive effects, this strategy has great potential and application for improving multimodal treatment of malignant tumors in the future.

In the clinic, certain diseases caused by genetic mutations or viral infections can alter the morphology and size of cells. Mueller matrix polarimetry is highly sensitive to microscale structural changes and can be utilized to detect cellular morphological alterations. Moreover, the extracting parameters from the Mueller matrix can serve as a key indicator for characterizing anomalous changes in cell morphology, demonstrating promising potential for application in disease analysis and diagnosis. Based on full polarization Mueller matrix imaging, this study proposes a characterization method for assessing red blood cell morphological changes induced by osmotic pressure. The results indicate significant variations in polarization parameters reflecting the morphological changes of red blood cells under different osmotic pressure regulations from sodium chloride solutions with different mass fractions. In addition, there are some differences in the sensitivity of different polarization parameters to cell deformation.

The single cell sorting technique is important for proper treatment strategies and prognosis. In this work, we develop a system to real-time measure the Mueller matrix of multi-angle polarization scattering from an individual cell. The method measures the Mueller matrix of polarization scattering at two different angles, 60° and 120°, and can effectively analyze the microstructure, morphology and complex refractive index of single cells. The principal advantage of the method is that the multi-dimensional polarization parameters can improve the accuracy of cell sorting and quantitative analysis. Our phantom experimental results validate the advantages of multi-angle polarization scattering measurements. And then, Experiments on subtype classification of Acute lymphoblastic leukemia (ALL) support the validity and feasibility of the method, indicating that the Mueller matrix of multiple polarization scattering angles have important potential applications in high-throughput cell sorting.

Lipid peroxidation, linked to neurodegenerative diseases and mitochondrial dysfunction, targets polyunsaturated fatty acids (PUFAs) in cellular membranes. RT001, a novel drug with deuterium-substituted linoleic acid, inhibits lipid peroxidation by slowing down the chain reaction initiated by reactive oxygen species. Studies suggest that deuterated PUFAs presented in a fraction of the total PUFA pool, provide cellular protection. To gain insights into the underlying mechanisms, we utilized stimulated Raman scattering (SRS) imaging to analyze the subcellular distribution of RT001 using yeast as a model system. The isotopic substitution of deuterium enhances chemical contrast, enabling the observation of drug delivery dynamics. Concentration- and time-dependent aggregation of RT001 in lipid droplets and cell membranes were observed. SRS imaging holds promise for investigating metabolic processes of deuterated drugs such as RT001, thereby facilitating drug screening and development.

During the caries treatment, three-dimensional measurement of tooth profile and caries location is one of key steps in the therapeutic process of caries. We propose a three-dimensional vision measurement of tooth crown and dental caries with line structured light. First, an integrated calibration using right angled spot target is executed and the parameters are obtained rapidly. Subsequently, the profile of the tooth crown is reconstructed and the dental caries area is identified. The results show that the reprojection error could reach tens of micrometers and the caries’ area is measured as ~ 2.83 mm2. These results indicate high potential in the diagnosis and treatment of dental caries.

Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have achieved great success in the field of ophthalmology and are essential in early screening and diagnosis. They could perform noninvasive structural and vascular imaging of the eye. However, most Chinese community hospitals do not have any OCT equipment, due to the high cost. Portable OCT equipment could provide mobile and shared medical diagnostic services as an ideal solution to above problem. Therefore, we design and demonstrate a portable SD-OCT/OCTA system for retinal imaging with A-scan rate of 120KHz, based on a broadband light source with center wavelength of 850nm and bandwidth of ~100nm. The proposed equipment, consists of a laptop and an OCT engine with 30×25×20cm3 size and ~5kg weight, could be carried by one person from a hospital to another. The portable system offers an axial resolution of ~3.8 μm, a transverse resolution of ~7 μm on retina and a wide field of view of ~60 degrees. The system parameters of scanning unit have been simulated and optimized using software to achieve focusing spot with diffraction limited size on retina. In a word, the proposed OCT/OCTA equipment offers a nice tradeoff between imaging performance and portability, therefore makes up for the shortcomings of traditional OCT and is hopeful to enable the sharing economy for OCT.

Striae gravidarum is a kind of striae atrophicae commonly observed in primigravid women. Striae gravidarum appear irregular purple or light red at the beginning of pregnancy, and gradually fade to silver bright stripes in the late pregnancy or postpartum. Currently, laser therapy can help with striae gravidarum, so it is crucial to know how to effectively assess striae gravidarum both during and after treatment. This research proposed a feature enhanced U-net architecture model for identifying severity of stretch marks. This approach initially extracts the single-channel grayscale from RGB images of striae gravidarum and enhances their contrast and saturation by using optimal adjustment parameters. Subsequently, a U-net model is employed to perform striae gravidarum extraction based on the enhanced images. The train dataset is 848 frames, and the test dataset is 95 frames. Compared to the traditional threshold segmentation methods, the proposed approach achieves higher accuracy in segmenting stretch marks across different pregnancies. Compared to the full-connection model method, the proposed approach not only demonstrates faster calculation speed and reduced data requirements but also achieves superior results in terms of segmentation and classification accuracy, which is of great significance for the construction of auxiliary diagnostic instrument for striae gravidarum treatment.

The rebound tonometer is commonly used for measuring intraocular pressure in children and patients with limited mobility due to its small size, portability, and non-invasive nature. In order to ensure the accuracy of its measurements, ISO 8612:2009 "Ophthalmic instruments — Tonometers" mandates that it undergo a clinical human eye comparison test against the Goldmann tonometer, which is considered the “gold standard” for intraocular pressure measurement. Additionally, a transfer standard device is required to assess the consistency of measurements across similar tonometer products. These transfer standard devices, known as simulated eyes, mimic the structure of the human eye to varying degrees of complexity depending on the specific tonometer being tested. This paper presents several solutions for rebound tonometer transfer standard device, including a water column simulated eye, a discrete rubber simulated eye, and a PDMS pressure-adjustable simulated eye.The design principles and pressure regulation of each simulated eye are described. Furthermore, tests are conducted using two different brands of rebound tonometers, and the measurement results are compared. It is important to note that each simulated eye has its own advantages and disadvantages due to its inability to fully replicate all parameters of the human eye. Addressing issues such as traceability and the aging effect of materials is crucial in improving the accuracy and reliability of these simulated eyes.

The fundamental principle of hepatectomy is to entirely excise the tumor while preserving adequate functional liver tissue volume. Thus, identifying tumor and non-tumor areas swiftly can enhance the precision and efficiency of liver resection, ultimately improving patient survival rates. In this study, we utilized multiphoton microscopy (MPM) to label-free identify liver tumor and non-tumor regions, following by automated classification with an open-source convolutional neural network, ResNet. The outcomes demonstrate that the network model can automatically and effectively distinguish tumor and non-tumor regions without human recognition, and MPM combining with deep learning may serve as an auxiliary tool for rapidly detection of hepatocellular carcinoma and aiding in liver resection treatment.

In-stent restenosis (ISR) and its associated inflammation remains a significant concern for long-term patient outcomes following stent implantation in percutaneous coronary intervention (PCI). The problem is intricately associated with endothelial injury, excessive endothelialization, hyperproliferation of smooth muscle cells, and the infiltration of inflammatory molecules. However, commonly employed imaging techniques encounter challenges in simultaneously acquiring both vascular structural information and functional data related to inflammation. Here, we presented a novel Tri modality intravascular imaging system capable of simultaneous optical coherence tomography (OCT), near-infrared fluorescence (NIRF), ultrasound (US) imaging, and fabricated the OCT-NIRF-US catheter which outer diameter is 0.75 mm, aiming to provide a more comprehensive diagnostic tool for ISR and its associated inflammation. Experiments were conducted on atherosclerotic rabbits implanted with a scaffold, divided into two groups (n=3 each group) for assessment on the first and twenty-eighth day, respectively. Primary results demonstrated that the integrated OCT-NIRF-US intravascular system enables complementary structural imaging and functional imaging of inflammation. The system presents the potential to offer a more accurate assessment, providing with valuable insights into the ISR processing and assisting in the development of more precise clinical strategies.

Accurate measurements of the algal cell concentration are essential in microalgae culturing and ecological monitoring. Here, we propose a method based on optical coherence tomography (OCT) to assess green algae concentrations quantitatively and measure the depth distribution of green algae cells. The amplitude information of the complex OCT sequence was extracted to calculate the scattering coefficient. The results show that the scattering coefficient can be used to evaluate different concentrations of green algae suspensions by frequency statistics under non-contact conditions, and the scattering coefficient images can provide the depth distribution of green algae cells. This method provides an in situ, accurate and non-invasive tool for monitoring the growth of green algae and can effectively reflect the health status of water bodies.

Background: Superconducting strip photon detector (SSPD) have been applied to monitor singlet oxygen ( 1O2) luminescence produced during photodynamic therapy (PDT) due to its high detection efficiency, low dark count and fast detection speed. Objective: This study calibrate of the detection efficiency and dark counts of a new SSPD system for singlet oxygen luminescence detection. Materials and Method: The input photons that passing through the attenuator, polarization controller and optical fiber were carefully controlled so that the average photon arrival-time interval was much larger than the response time of SSPD detector. The electrical signals generated from SSPD detector were collected after passing through a low-noise amplifier and analyzed using data acquisition card. Bias current started at 10 μA and increased by 1 μA step until saturation. The electrical signal pulses were recorded. The detection efficiency was determined by the ratio of the electrical signal pulse count minus the dark count to the number of photons input to the detector. Dark counts were measured by adjusting the bias current with the light input channel completely closed and in a dark environment. The experimental temperature was controlled at 2.2 K. Result: The best detection efficiency was achieved when the bias current was 23 μA with the input photon power of -107.6 dBm. The detection efficiency was 88% and 90% for two tested detectors with the optimized polarization. The dark count rate was less than 100 cps. Conclusions: This study demonstrated that the optimization of attenuator, polarization controller and bias current can improve the detection efficiency of SSPD system for singlet oxygen detection.

Currently, there are two backscattering polarimetry schemes, namely the collinear reflection mode with normal incidence, and the non-collinear reflection one with obliquely incident illumination. To eliminate the surface reflection from the sample cost-effectively, the non-collinear backscattering Mueller matrix (MM) imaging setup always has an oblique incidence. Meanwhile, the uneven tissue surfaces can induce various relative oblique incidences inevitably, which can affect the polarimetry in a complicated manner. In this study, we systematically measured the MMs of experimental phantom and ex-vivo tissues under different oblique incident illuminations. Moreover, a Monte Carlo simulation program based on cylindrical scattering model was also adopted for further analysis and validation. Oblique incidence can induce various aberration on non, two and four periodic MM elements, leading to false-positive and false-negative information for tissue polarimetry. The findings presented in this study give the suggestions for appropriate incident angle selection in in vivo tissue polarimetric practices and can also be useful as valuable references for studying how to minimize the influence furtherly.

Background and Objectives: Area quantification has always been of research interest as it is an important step in assessing the treatment efficacy of port-wine stains (PWS) birthmarks. The objective of this study was to investigate the potential application of cellphone camera combined with manual labeling for area quantification. Materials and Methods: First, the color of the untreated PWS lesions was measured and RGB values extracted. Then, a total 48 PWS patches to represent different color, shape and size were printed. Each PWS patch was placed on a 3D head model along with a 1 cm × 1 cm size reference for photographing using a cellphone camera. The lesion region and the small white square region were determined by manual labeling method. The number of pixels within the lesion region and the small white square region were counted and the area of the PWS lesion was calculated. In addition, the image assessment was also compared with the traditional grid counting methods. Results: Each PWS patches were photographed three times. The results showed that the average error rate of the proposed digital photo scheme for the area quantization was 0.132±0.055 and the average error rate of the grid counting scheme for the area quantization was 0.004±0.005. Conclusions: Preliminary study demonstrated that the use of cellphone photo scheme to quantify the area of PWS was feasibility but its error rate was higher than that of the grid method.

The cytoskeleton plays an important role for maintaining cell morphology and movement. Extracting the fine feature of cytoskeleton would be helpful to investigate the role of cytoskeleton in biological and pathological process. This paper proposes a feature analysis method based on machine vision to extract the fine feature of cytoskeleton. The nasopharyngeal epithelia cells (NP69), human high metastatic nasopharyngeal carcinoma cells (5-8F), and human high differentiation nasopharyngeal carcinoma cells (CNE1) were imaged with a total internal reflection fluorescence microscope (TIRFM). To extract the fine feature of cytoskeleton from these TIRFM images, three machine vison methods including the conventional threshold method, the adaptive threshold-connected-domain method (ATCD), and the maximum interclass variance method (OTSU) were used to segment the cytoskeleton. The ATCD method has great advantage over the conventional threshold and OTSU. The extracted length of the nasopharyngeal carcinoma cells (5-8F and CNE1) cytoskeleton is shorter than that of nasopharyngeal epithelia cell (NP69). These results indicate that ATCD machine vison method can be used to extract the fine feature of cytoskeleton with high resolution TIRFM images.

In the last decade, there has been a growing interest in dual-modality imaging techniques that combine intravascular ultrasound (IVUS) with optical coherence tomography (OCT) for intravascular imaging. However, accurately quantifying the vulnerability of coronary atherosclerotic plaques solely through structural imaging methods remains challenging. Optical Coherence Elastography (OCE) technology, which assesses tissue elasticity by analyzing tissue displacement and strain distribution, has emerged as a promising approach. In recent years, the field of intravascular optical coherence elastography has shown significant progress, and its potential applications are becoming increasingly evident. In this study, we developed a comprehensive imaging system capable of IVUS-OCT-OCE tri-modality imaging for both phantom and biological tissues. Preliminary results indicate that the system exhibits high-quality imaging capabilities and offers precise identification of plaque tissue. This presents a novel approach for characterizing plaque tissue within blood vessels and holds promise for early diagnosis in coronary atherosclerosis

Intense pulsed light (IPL) is an incoherent high intensity pulsed light source and commonly used in dermatology and aesthetics. Early studies suggest that the combination of IPL and photosensitizer can induce cellular phototoxicity. Hemoporfin is a domestic photosensitizer approved for PDT treatment of Portwine stain birthmarks. This in vivo study evaluated the feasibility of using IPL as a light source for the excitation of Hemoporfin in mouse model. Results showed that IPL+Hemoporfin could cause more severe photodamage than IPL alone group, which suggests that it might be feasible to use IPL as PDT light source.

The third-order nonlinear nature of Broadband CARS means it has the inherent capability to produce label-free images using vibrational information as contrast. The speed of the technique is several orders of magnitude greater than spontaneous Raman spectroscopy. This has implications for enabling diagnostics in areas such as histology, immunology and cytology. The major drawback in BCARS currently preventing these applications is mainly the nonresonant background, present due to degenerate four-wave mixing. This background can be removed using the tensorial properties of the electronic susceptibility. This technique is known as spectral interferometric polarization CARS (SIP-CARS). We show an implementation of SIP-CARS on highly resonant polymer beads for spectroscopic imaging using two sequential BCARS scans to probe different components of the susceptibility tensor.

Alzheimer's disease (AD) is a common neurodegenerative disease, whose early diagnosis is crucial for disease control and treatment. This study aims to explore the use of ensemble learning to analyze data from AD patients using multimodal inputs, including MRI image features extracted by convolutional neural networks (CNN), age, gender, APOE status and clinical functional scales. Firstly, we preprocess and extract the key image information features related to AD from MRI images. We then used multiple machine learning (ML) methods to build different classifiers, and combined these different classifiers by voting to obtain more accurate prediction results. Our method has been validated on a large AD patient database.The results demonstrated that the analysis of multimodal data can significantly improve the diagnostic accuracy of AD compared to single-mode data, while ensemble learning further improves the stability of the model.

The application of Broadband CARS to cell imaging studies has thus far been limited to those where high contrast features are present, such as lipids and exogenously introduced tags. This is due to the inherent low SNR obtained in BCARS from the low density of oscillators in single cells coupled with the non-resonant background present in all media which distorts the measured signal. In this paper, we show that an autoencoder which we named VECTOR2, trained on simulated spectra, can accurately perform NRB removal of recorded BCARS images of unstained biological specimen. This allows cell imaging comparable in time to spontaneous Raman imaging with high bandwidth and resolution. The introduction of standard baseline flattening prior to NRB removal preserves the image structure while removing artefacts from raster scanning and optical noise. This results in a hyperspectral image of the NRB-free BCARS signal which is linear in the sample concentration and has a spectrum that is very similar to the spontaneous Raman spectrum.

Ischemic cerebral edema (CE) is a major cause of death in patients with ischemic stroke. Detecting CE and local cerebral blood perfusion (LCBP) simultaneously may offer valuable insights for treating ischemic stroke (IS). In this work, a swept-source optical coherence tomography (SS-OCT) system was established to evaluate the photo-thrombosis ischemia mouse model for a long time. The progressive state of CE and LCBP was quantified and analyzed. Preliminary studies revealed that edema significantly affected LCBP with a clear spatial dependence, the edema area increased sharply when the LCBP dropped to around 60% of the initial value.

This study utilized 248 white light endoscopic images and 258 narrow-band endoscopic images of early-stage esophageal cancer provided by Kaohsiung Medical University. After data processing and annotation, the images underwent augmentation and hyperspectral transformation. The dataset was then divided into training and testing sets and fed into the YOLOv5 model for the detection of esophageal cancer lesions. Four models were developed to predict the location of esophageal cancer lesions: separate models for white light and narrow-band images, as well as models for RGB and hyperspectral images. The results were evaluated based on the severity of the images, categorized as normal, dysplasia, and squamous cell carcinoma (SCC).According to the analysis of Dysplasia sensitivity, the sensitivity was 70.4% for white light esophageal cancer images, 79.5% for narrow-band esophageal cancer images, 84.3% for white light hyperspectral esophageal cancer images, and 84.5% for narrow-band hyperspectral esophageal cancer images. According to the analysis of SCC sensitivity, the sensitivity was 75.8% for white light esophageal cancer images, 81.3% for narrow-band esophageal cancer images, 86.6% for white light hyperspectral esophageal cancer images, and 86.9% for narrow-band hyperspectral esophageal cancer images. The results indicated that the model performed better in detecting SCC than Dysplasia, and narrow-band images had higher accuracy compared to white light images.Regarding mAP analysis, the mAP was 72.7% for white light esophageal cancer images, 86.8% for narrow-band esophageal cancer images, 78.6% for white light hyperspectral esophageal cancer images, and 88.6% for narrow-band hyperspectral esophageal cancer images. In conclusion, hyperspectral images demonstrated higher mAP values in both white light and narrow-band settings, indicating a significant improvement in the detection of esophageal cancer lesions compared to RGB images in this study.

Various algorithms have been proposed to compute attenuation coefficient (AC) based on optical coherence tomography (OCT) signals. The single scattering model has gained widespread application due to its computational efficiency; however, it’s only applicable to thin tissue layers, thus imposing strict requirements on the data length used for calculations. This study compared the algorithms for estimating the AC from OCT data using different data length such as 180 μm , 250 μm , 350 μm , 435 μm acquired from intralipid phantoms. Subsequently, employing various algorithms and utilizing the optimal data length, we calculated the attenuation coefficients of liquid phantoms with different concentrations, and the methodology was applied to homogeneous mouse tissues. The comparative study may provide a reliable basis for parameter configuration for AC extraction algorithm.

Tuberculosis is still one of the leading causes of death in the world. The COVID-19 pandemic increased the number of newly diagnosed cases, bringing it to 6.4 million in 2021, the number of deaths raised up to 1.4 million. An estimated 10.6 million people fell ill with TB worldwide in 2021, an increase of 4.5% from 10.1 million to 10.6 million was detected. Since the diagnosis of tuberculosis pathology can reach a sufficiently large amount of time, the task of rapid identification of both tuberculosis pathology and the antibiotic resistance of the mycobacterium becomes paramount. Methods based on real-time PCR are used as classical microbiological technologies, however, they have significant limitations in the number of antibiotics for determining resistance. As the vibration spectra (IR or Raman) of Mycobacterium tuberculosis cell is complex and it is difficult to find specific biomarkers for cell fast differentiation.

Efficient detection of cell clusters is of paramount importance in the production and packaging processes of single-cell suspensions, as it can significantly impact cell density uniformity, induce apoptosis, and compromise the accuracy of cell sorting outcomes. This study introduces a novel, high-throughput and label-free method for monitoring cell clusters, leveraging light scattering imaging and microfluidic technologies. Two-dimensional (2D) light scattering, as a vital label free analytical approach, proves effective in detecting and analyzing biological particles with intricate structures. Through optical microscopy coupled with a high-speed camera, this study examines the 2D light scattering patterns produced by cell clusters, facilitating the discrimination between single cells and clustered cells. This label-free methodology offers distinct advantages over traditional labeling techniques, as it preserves cellular integrity without invasive disruptions. Furthermore, innovative microfluidic chip design enables continuous real-time monitoring of cell clusters, empowering rapid and high-throughput detection. Experimental validation involved monitoring silicon oxide (SiO₂) microsphere suspensions, demonstrating the method's capacity for high-throughput and high-sensitivity cell cluster monitoring. This research presents a promising tool for efficiently handling and monitoring the production of single-cell suspensions, with potential applications in various fields of cell biology and biotechnology.

Lymphomas encompass Hodgkin lymphoma and non-Hodgkin lymphoma. In this study, we have developed a static cytometry leveraging laser and microscope technology to capture 2D light scattering patterns of individual cells. Within this method, a single lymphoma cell is positioned in a liquid-based chip and vertically stimulated by a 532 nm green laser. The resulting light scattering pattern of the cell is observed and recorded by a COMS detector through a microscope optical system, covering a polar angle range of 75 to 105 degrees. By extracting and analyzing the characteristic values from these scattering patterns, we can achieve lymphoma cell identification. In this study, we successfully differentiated between HDLM-2 and Daudi cells using the SVM algorithm, achieving a classification accuracy of 88%. This outcome underscores the potential of our 2D light scattering static cytometry for lymphoma cell classification, offering a marker-free, cost-effective approach for early cancer screening at the single-cell level.

Optical coherence tomography angiography (OCTA) is a non-invasive, high-resolution imaging technique which visualizes capillary vasculatures. However, up to now, no accurate quantitation of capillary velocity based on OCTA images has been reported. Currently study of capillary velocity was substantially performed based on the empirical correlation curves between OCTA signals and blood flow velocity in respective OCT setups and OCTA algorithms. In this study, we proposed an accurate measurement of the capillary velocity in blood vessels using microbubble localization and tracking imaging. Based on localization and tracking of microbubble, motion of microbubble in the blood vessel was identified and parameters of vasculature such as the direction and velocity of capillary flow were subsequently obtained. In this prediction study, a flow phantom using the polystyrene microsphere and a cellulose tubing with the internal diameter of 300μm was used to simulate the flow in a single blood vessel. The flow velocity was set to be 2-6μL/min. In the experiment, 5% milk solution and microsphere suspension were injected into the cellulose tubing using a precision syringe pump. In OCT performance, B-scan imaging was set at a frame rate of 500 frames per second (fps). In each acquisition, 1000 effective frames were acquired, which corresponded to an ultrafast data acquisition of 2s. Speckle Variance (SV) algorithm was employed to remove background tissue signals and extract the flowing microsphere signals in OCT data. The center points of the microsphere were determined by identifying the local maxima of the cross-correlation coefficient. Additionally, the Kuhn-Munkres (KM) assignment algorithm was employed for the purpose of tracking microsphere. Distances between pairs of consecutive frames were then calculated for all microsphere. Ultimately the velocity magnitude map was generated. preliminary results on blood vessel phantom validated the feasibility of the proposed technique, which will be further performed in quantitation of capillary velocity in living tissues.

The small-field radiotherapy is a developing technique because it can reduce the damage to normal tissues. However measurement of the dose distributions of small radiation fields in radiotherapy is a challenge. In this work, we designed and produced an optic fiber X-ray sensor array with high spatial resolution. The sensing array includes 7 sensing probes connecting to an 8-channel optical switch and a photon counting detector (PCD). To verify the practicality of the system, these sensors were measured under a 10×10 cm² field of a medical linear accelerator. For the small-field application, the dose distribution of radiotherapy fields 1×1 cm² and 0.8×0.8 cm² were measured. The distribution of these small radiotherapy fields were given based on the experimental results.

In this study, we proposed a fast endoscopic OCTA using proximal scanning catheter based on B-scan scheme and a customed image registration algorithm. Image registration based on the similarity between adjacent B-scan frames was utilized to reduce the impact of external motor vibrations and improve image quality. Based on the registered images at the same scanning position, the speckle variance (SVAR) algorithm was employed to calculate variation of OCT intensity signals. Finally, en face OCTA images were generated. Preliminary results of endoscopic OCTA performance using microfluidic phantom proved the feasibility of the proposed technique.

In this paper, the long-term short-term memory (LSTM) architecture is investigated as a tool for wavelength calibration of a spectrometer. Polynomial fitting is the most common method of wavelength calibration, whereby wavelength standards, such as neon and krypton are recorded and the position of the spectral lines on the detector together with the known wavelengths are used for fitting. The method performs poorly when only a small number of lines appear within the bandwidth recorded by the spectrometer. We demonstrate how the basic encoder-decoder LSTM architecture can be used to provide superior wavelength calibration accuracy when five or less lines are present. We believe with further development, machine learning could outperform the traditional methods in all cases.

An extended calibration procedure is required to collect sufficient data for establishing a stable and reliable subject-specific classifier before the user can use a brain-computer interface (BCI) system based on functional near-infrared spectroscopy (fNIRS). In addition, individual differences can lead to low generalization performance of the subject-specific classifier cross-subject. To address the above dilemma and improve the universality of the fNIRS-BCI system, we propose a revised Inception-ResNet (rIRN) model-based transfer learning (TL) to improve the cross-subject decoding accuracy of mental tasks. The TL-rIRN is a deep transfer learning model that combines an elaborated rIRN model for fNIRS signal classification with model-based transfer learning. The fNIRS data of eight participants are collected for the purpose of distinguishing between mental arithmetic and mental singing tasks. The leave-one-subject-out cross-validation method is used to evaluate the cross-subject decoding performance of TL-rIRN. The results show that TL-rIRN improves cross-subject decoding accuracy and effectively reduces model training time, calibration time, and computational resources.

Cancer is one of the world’s leading causes of death. Non-invasive, highly sensitive detection of cancer has thus been a crucial area of research. This work presents five theoretical designs of one-dimensional defective photonic crystals for detection of cancerous cells. Proposed designs are modeled theoretically on Matlab using the Transfer Matrix Method. Layer thicknesses in each design are optimized for maximum sensitivity. The designs are evaluated and compared based on sensitivity, quality of defect modes, immunity to fabrication errors in thicknesses, and stability under uncertainties in the sample’s refractive index. Theoretically, the design with structure Air/(TiO2/sample)5/(sample/TiO2) 5/Air has a high sensitivity of 1700 nm/RIU and good tolerance for errors. The proposed biosensor can potentially be used in large-scale cancer detection.

Purpose: Use the OCTA to figure out the relationship between PWS structure and therapeutic effect. Method: Before and immediately after V-PDT, OCTA was used to scan the skin of the same part of 80 patients. The efficacy was evaluated colorimetrically 3-6 months after V-PDT. The relationship between PWS lesion structure and V-PDT efficacy was analyzed. The lesion structural factors affecting the prognosis of V-PDT were analyzed using a multi-factor binary Logistic regression model. Result: Among the total 80 PWS patients who received V-PDT, 22 were non-responders and 58 got improved. Comparing the structure in non-responders and improved patients, non-responders had a thicker epidermis (P=0.0012), and a higher proportion of 10-30μm vessels (P=0.0014), higher average density ratio between shallow and deep layers (P=0.0006), higher proportion of vertical vessels (P<0.0001), and no decrease of number of vessels immediately after V-PDT (P=0.002). Conclusion: Multiple structural factors can significantly affect the efficacy of V-PDT.

Tissue classification in surgical workflows between healthy and tumoral regions remains challenging both during and post-surgery. The current standard practice consists of taking small biopsies directly after tumor resection and sending them to pathologists for an intraoperative margin assessment, which is time-consuming and error prone due to the necessarily limited size and number of samples. Then, after the surgery is completed, the resected tumor is sent to the pathology lab, where its type and grading are further confirmed. The present workflow is prone to inaccuracies and particularly difficult when the sample is resected in several pieces. Therefore, an intraoperative tissue classification technology is highly sought-after for a simplified surgical workflow and better patient outcome. Our work aims at using hyperspectral images (HSI) for contact- and tracer-free tissue differentiation. We introduce a deep learning-based algorithm for the classification of tissue type that is based on spectral information and can be applied simultaneously to the whole sample. We illustrate the performance of our method on ex vivo head and neck squamous cell cancer samples. The proposed algorithm can differentiate between three main classes: background, tumor, and healthy tissues. Our experiments first assess the generalization of the neural network on data from unseen cases. We then determine the minimal number of training examples needed to cover the variety of tissue spectral appearances seen in the clinical dataset. We evaluate the influence of the delay between resection and start of image acquisition on the quality of the recorded HSI and the prediction. Qualitative and quantitative evaluations support the applicability of hyperspectral imaging for tissue classification and demonstrate an agreement between surgeon annotations and neural network predictions in most test cases.

Skin temperature fluctuations cannot be ignored in non-invasive human blood composition measurement based on near-infrared spectroscopy. To effectively address and correct for the interference caused by skin temperature, it is essential to understand the spectral characteristics induced by changes in skin temperature. In this study, we simulated the three-dimensional temperature distribution of a five-layered skin model. The goal was to examine how the skin temperature varies with changes in the environmental temperature and the body's core temperature. Additionally, Monte-Carlo simulation was used to obtain the diffuse reflectance spectra. The corresponding spectral characteristics were analyzed. This study can be useful for temperature calibration in non-invasive blood composition measurement in humans.

Visualization of vessels within the tumor is of great importance for the diagnosis and control of the effectiveness of therapy of neoplasm. 3D OCT in comparison with dynamic, although it does not have the ability to detail the entire complex of neoplasm vessels, it still has some advantages. Namely, it is able to quickly and without additional processing provide data not only about the vessels, but also about the structure of the neoplasm itself. The purpose of the study: To determine the possibility of using 3D OCT to study the condition of skin vessels in neoplasms. Materials and methods. The work used an optical coherence tomographs for noninvasive examination of the internal structure of human surface tissues "OCT-1300-E", special programs HF_OCTf_512Cuda+ViewKub, HF_OCTf_Viewer and Ratio Square Medical Image. A study of healthy skin and skin in the lesions of 46 patients with skin neoplasms (12 patients with basal cell carcinoma (BCC), 10 with nodular melanoma, 4 with keratoacanthoma, 5 with T-cell lymphoma, 10 with venous hemangioma, 5 with sebaceous gland hypertrophy) was performed. Results. The results of the analysis of images of vertical skin sections within the edge and central part of the superficial form of basal cell carcinoma obtained during the examination of 12 patients revealed typical OCT signs for this disease in the form of a lack of visualization of layers in the tumor area, a significant increase in the total height of the layers with an increase in the intensity and depth of the useful signal. The analysis of 85 images of vertical sections of the central and peripheral parts of nodular melanoma obtained during the examination of 10 patients revealed OCT signs of this disease in the form of an increase in the total height of the image layers with the absence of visualization of the layers themselves, a significant increase in the depth of the useful signal, but a weakening of its intensity within the tumor . As in the case of basal cell carcinoma, numerous dilated vessels were visualized within the tumor, the concentration of which was higher in the peripheral part of the neoplasm. Conclusions. Based on the conducted studies, it can be concluded that 3D OCT is an effective method of lifetime assessment of the morphological state of the microcirculatory bed of the skin in dermatoses of various nature. Assessment of the condition of blood vessels by the 3D OCT method can have both diagnostic significance and play an essential role in understanding the pathogenetic mechanisms of the disease due to the fact that the method allows us to judge, on the one hand, the predominant lesion of vessels of a particular caliber, on the other hand, the inflammatory or non–inflammatory nature of vascular pathology.

There is a problem of the shallow depth of the OCT probing radiation, in this regard, it seems promising to study the possibilities of its combination with high-frequency ultrasound in assessing the depth of skin damage. 15 patients of the Burn Center of the Nizhny Novgorod Research Institute of Traumatology with burns of varying severity, were examined. Thus, high-frequency US is a very effective method that allows to get an answer to topical issues of Combustiology, in particular to determine the safety of appendages, which allows to differentiate burns as 3a or 3b, to fundamentally determine the presence or absence of the epidermis, to distinguish second and third degree burns, but does not provide detailed information about the epidermis, the zone of the epidermal-dermal junction, the characteristics of the microcirculatory bed, so the method can be used to determine the depth of skin damage without doubt, but the best results can be achieved with a combination of two techniques.

Laser procedures are very popular. But the effectiveness of most skin rejuvenation procedures has not been proven. Purpose: to determine the true picture of morphological processes occurring after exposure to laser radiation on the skin. Materials and methods: The work uses HiFU with a 75 MHz sensor, Er:glass laser. 15 patients aged 35 to 65 years were examined. For anesthesia numping cream with lidocaine 2.5% was used. Results: after application anesthesia, the signal intensity within the epidermis significantly decreases, which indicates the presence of signs of edema in this layer. Immediately after the procedure, pronounced signs of edema in the dermis were noted in 100% of cases. A month after the procedure, there was an increase in signal intensity within the dermis. Conclusions: As a result of laser exposure the changes manifest themselves in the production of collagen, and not in the formation of focal fibrous changes. The use of diagnostic methods will improve the effectiveness of procedures

The lack of intact skin in extensive burns makes it necessary to repeatedly use donor sites to obtain grafts. This is possible if there is sufficient thickness and structure of the donor site skin. The thickness and structure of the donor site skin may deteriorate due to sepsis. Purpose: to study the structure of the donor site skin in patients with burns and sepsis using Ultra-High Frequency Ultrasound (UHFUS) data. The study involved 44 patients with skin burns: 30 without sepsis, 14 with sepsis. Donor site was examined 12 days after graft harvesting. Methods - UHFUS (75 MHz), histology, immunohistochemistry. Results. Donor site without sepsis was characterized by thin epidermis and low overall skin thickness (72% compared to intact skin). At the same time, the papillary layer was increased to 127% (p=0.022). In patients with sepsis, all skin layers, including the papillary dermis, were thinner than in intact skin and thinner than in patients without sepsis. Histomorphometry confirmed UHFUS results.

At present, medical endoscopy is the main procedure for exploring the internal cavities of the human body. Developing methods for integrating image processing and endoscopic visualization improves image quality and accurately identifies cancerous abnormalities. This paper aims to improve the efficiency detection of gastrointestinal disease detection in the endoscopic video. A recognition method is proposed, which is based on a 3-D binary difference of micro blocks and consists of the following steps: 1) a sequence of frames is divided into 3-D cuboids; 2) inside each cuboid, patches of different sizes are built, which are used to obtain volumetric local binary templates; 3) the Hamming distance is calculated between a randomly selected pair of cuboids within each patch of the video clip, which is sequentially written to a separate vector descriptor; 4) for recognition, the method of machine learning is used. Experimental studies show that disease detection is performed accurately on the test dataset (Kvasir).