This PDF file contains the front matter associated with SPIE Proceedings Volume 12320, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

With multiple mechanisms of contrast, high sensitivity, high resolution, and the possibility to create miniature, inexpensive devices, light-based techniques have tremendous potential to positively impact cancer detection and survival. Many organs of the body can be reached in a minimally-invasive fashion with small flexible endoscopes. Some organs, such as the fallopian tubes and ovaries, require extremely miniature (sub-mm) and flexible endoscopes to avoid tissue cutting. Additionally, some modalities, such as side-viewing optical coherence tomography, are naturally suited to miniature endoscopes, whereas others like forward-viewing reflectance or fluorescence imaging, may require performance tradeoffs. The development of small, robust and fiber-delivered advanced light sources, miniature fiber bundles, and sensitive detectors has aided the development of novel miniature endoscopes. We have incorporated these technological advances to build endoscopes for challenging applications, especially early detection of ovarian cancer.

Cholesterol plays a very important role in human physiological function, and the level of cholesterol in human body is a marker for diagnosing a variety of diseases. The article proposes a localized surface plasmon resonance (LSPR) sensor based on dual-tapered optical fiber (DTOF) that can detect cholesterol concentrations in the human body. To excite the LSPR effect and improve the sensing probe's sensitivity, gold nanoparticles (AuNPs) were immobilized on the surface of the DTOF. In this work, the specific reaction between cholesterol and cholesterol oxidase led to the change of refractive index (RI) near the sensing probe, and the corresponding spectrum is collected. Additionally, the performance of the sensor was evaluated, including linear range, reproducibility, reusability, stability, and selectivity. The experimental results demonstrate that the proposed DTOF-based LSPR sensor is capable of detecting the cholesterol level accurately and has a promising application in biomedicine.

Cerebral edema is a potentially fatal disorder and divided into several types. The accurately identifying the type of cerebral edema is vital for the precise treatment. Previous studies have been devoted the multi-model parameters to access the feature cerebral edema. However, few research focused on the models to distinguish the types of cerebral edema. Here, a multi-parameter joint imaging system was developed, which combined laser speckle contrast imaging system (LSCI) and intrinsic optical signals imaging system (IOS). Several parameters such as relative cerebral blood flow (rCBF), concentration changes of oxy-hemoglobin (HbO), deoxy-hemoglobin (HbR), and blood oxygen saturation (sO₂) were recorded at the same region of interest (ROI). Vasogenic and cytotoxic cerebral edema models were induced by phenylephrine hydrochloride and vasopressin, respectively. The mice were randomly assigned to 2 groups and 10 individuals in each group. After drug injection, the parameters were collected every 20 minutes within 2 h in vivo. The rCBF in both groups increased gradually within 2 hours throughout the whole process, but the rCBF in the vasogenic group grew substantially faster than it in the cytotoxic group within the first 80 minutes. In the vasogenic group, the concentration changes of HbO and HbR were more pronounced. The data was analyzed by the t-test of an independent sample, and there were significant differences between the parameters of the two types of cerebral edema. It has potential for achieving a classification of cerebral edema.

Distributed sensing technology provides engineers with powerful tools for position sensing, 3D sensing, shape sensing, and model validation applications. A new method for fiber-optic 3D shape sensing that can be used in minimally invasive biomedical devices is presented. A shape sensor was made using a heat-shrink tube, a Teflon hard tube and three strings of fiber Bragg grating (FBG). The sensor has compact structure, good flexibility. Besides, a shape reconstruction algorithm is established, for the location of each FBG array on the sensor, the calculation method of curvature and torsion is designed, and the continuous functions of curvature and torsion are constructed by cubic spline interpolation. The shape reconstruction under different bending conditions is realized by using the coordinate system rotation method. In the error analysis stage, the curvature error and torsion angle error of the sensor are optimized by introducing the scale factor. The results show that the optimized algorithm has good shape reconstruction effect.

Polarimetric imaging can provide morphological and structural information of tissue and has demonstrated applications during endoscopy. Polarimetric endoscopy has been shown by conducting polarimetric imaging through customized rigid endoscopes. Here we demonstrate Stokes polarimetric imaging via a commercially available rigid endoscope that can acquire full-Stokes information in a snapshot. This system incorporates two division of focal plane linear polarization cameras, a waveplate and a commercially available rigid endoscope. In addition, the sapphire windows in rigid endoscopes are known for their polarization effects. In this work, the polarization properties of the sapphire windows are carefully measured and calibrated to guarantee the accuracy of the system. Quantified analysis of the system and the imaging results on tissue are provided.

Polarization measurement technology can detect the microscopic structure information from biological samples. However, for biological samples with weak scattering effect, the optical signal obtained by the detector will contain a large amount of zero-frequency light without scattering, which makes it difficult to effectively extract the polarization information of the samples with weak scattering effect. Therefore, in this study, we introduce spatial filtering technique into polarization measurement. In the experimental setup, a spatial filter is placed in the rear focal plane of the objective lens to block the zero-frequency light, so as to realize the selection of scattered photons. A red LED with the wavelength of 633nm was used as the lighting source. The measurement of polarization information is based on the dual-rotating quarter-wave plate method. Considering the widespread presence of spherical and columnar scatterers in biological systems, Hacat cells and biological fibers were selected for the experiment. By comparing with the polarization data without spatial filtering, we found that the polarization parameter obtained after the high pass filtering measurement can better characterize the samples.

Polarization is a non-invasive optical technique and has become an emerging new tool in biomedicine studies due to its sensitivity to microstructure. The development of fast polarization techniques has enabled the application of polarimetry not only include static samples such as pathological sections, but also contain dynamic samples such as the process of tissue optical clearing (TOC). Dehydration is one of the important mechanisms of TOC which can result in the changes in tissue anisotropy, samples with different structures will show different characteristics in dehydration. In this study, three kinds of tissues with different degree of anisotropic including skeletal muscle, skin and fat are employed. We extracted and compared the temporal polarization parameters of several samples which may be related to the properties of microstructures. Monte Carlo (MC) simulation was combined to further explore the relationship between temporal polarization parameters and anisotropy of the sample. We first verified the main mechanisms of dehydration in spherecylinder birefringence model (SCBM). Then the optical properties of the microstructure in the model were changed to explore their influence on the polarization parameters. By analyzing the polarization parameters extracted from temporal Stokes images and Monte Carlo simulation, we can present polarization parameters to characterize microstructures of biological tissues.

3D visualization of the intact vasculature is crucial for understanding the pathogenesis of different neurological and vascular diseases. Although various fluorescent vessel labeling methods have been already used for overall visualization of different vascular networks, little has been done to quantify the labeling performance of each vessel labeling method, as well as their applicability with various clearing protocols. In this study, we systemically compare the labeling performance of four vessel labeling methods on different mouse organs. We also quantitatively evaluate the compatibilities of the four vessel labeling methods with several typical tissue optical clearing methods. Finally, we select the optimal combination of tested vessel labeling and clearing method to perform 3D reconstruction of vascular networks within several typical organs. This study is expected to provide important information for choosing proper pipeline for 3D visualization of different vascular networks.

Mueller matrix imaging contains abundant microstructural information of the sample and provides quantitative characterization of anisotropic structures. In contrast to non-polarized microscopic imaging, Mueller matrix imaging can obtain basic structural information of the sample, such as fiber orientation and density, from polarization parameter images at low resolution. To analyze the combined effect of imaging resolution on Mueller matrix polarization properties, we first measured Mueller matrix images of unstained collagen fiber-rich rat dorsal skin sections using a range of objective magnification or numerical aperture (NA) values. Quantitative analysis of first-order moments and image texture parameters were then compared in conjunction with polarimetry basis parameters images. The results show that the polarimetry basis parameter images preserve most of the structural information of the sample and can provide fast imaging speed. This study provides a criterion to determine which structural information can be accurately and rapidly obtained by transmission Mueller matrix microscopy with low NA targets to aid in pathological diagnosis and other applications.

Zebrafish has become a popular and highly effective animal model for human disease studies and biomedical discovery. Though multiple techniques have been developed for testing its resistance to environmental, toxicological and other stressors. For in vivo studies of the side effects of toxicants on particular organs, non-invasive imaging techniques are necessary. For example, optical tracking the variations of transmitted light intensity allows accurate blood vessel mapping of the whole animal while monitoring the polarization state of light allows muscles imaging. In this study, we propose to combine these two techniques into one using a Polarsens sensor with 4-directional on-chip polarizer. It provides singleshot acquisition of 4 multi-directional polarized images and calculating both temporal and polarization properties of light interacted with the specimen. Multiple experiments on zebrafish embryos demonstrate high efficiency of this approach for simultaneous hemodynamics and muscular structure analysis. We believe it might be highly effective for in vivo quantifying pathological reactions to various stressors in cardiovascular and skeletal muscle systems.

Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.

Perineural invasion, which is characterized by tumor invasion and spread along nerves, is an important pathological feature of many malignant tumors and is considered to be an important prognostic indicator. Therefore, it would be meaningful to rapidly and directly identify perineural invasion for pancreatic cancer patients. In this work, we try to use multiphoton microscopy (MPM) to detect perineural infiltration in pancreatic cancer. Our aim is to determine whether perineural invasion can be label-freely identified using multiphoton imaging, and experimental results show that MPM is able to quickly and accurately detect this pathological change in the tumor microenvironment without the use of exogenous contrast agents and even can identify different extent of perineural invasion. This study demonstrates MPM has the potential to be an alternative tool to provide a new method to monitor perineural invasion.

The rapid and accurate identification and classification of cell types is of great significance in scientific research and clinical diagnosis. We proposed the wide-field hyperspectral interferometry rapid label-free (WHIRL)imaging method. Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. In machine learning, support-vector machine (SVM) is one of most commonly used supervised learning models. We cultured a variety of cells and performed hyperspectral imaging by spot scanning with a spectrometer. Then use the SVM method for classification. The accuracy of distinguishing benign from malignant is >99%, and the accuracy of distinguishing different lung cancer subtypes is >90%，which indicates a promising prospect for cell identification and classification based on WHIRL imaging method.

Tumor cell invasion and metastasis is closely related to various components in tumor microenvironment, and the spatial distribution of tumor cells may be linked to the tumor progression. The directionally distributed collagen fibers would lead to one-way migration of tumor cells, and misarranged collagen fibers allow tumor cells to multi-directional migration in tumor boundary, and therefore tumor cells present in different spatial distributions in the microenvironment. In this study, the open source convolutional neural network Hover-Net was used for segmentation and classification. After accurately segmenting tumor cells, spatial features were extracted. Our results indicated that there were significant differences in the spatial distribution of tumor cells in the two modes. These were further demonstrated to be potential different prognostic patterns.

Heterogenous zonation plays a crucial role in liver physiology and pathology. Extracellular collagen fiber deposition and intracellular lipid accumulation are hallmarks of many liver diseases, but their zonal distribution and pathological significance remains unknown. Here, we established a label-free multimodal nonlinear optical (NLO) microscopy that integrated stimulated Raman scattering (SRS) imaging and compositional analysis of neutral lipids, second harmonic generation (SHG) imaging of collagen fibers, and two-photon excited fluorescence (TPEF) imaging of auto-fluorescent metabolites to study liver zonation in situ. By analyzing the liver tissues obtained from carbon tetrachloride (CCl4) induced mouse centrilobular fibrosis model, our imaging data revealed that the amount of collagen fibers increased rapidly in central vein (CV) area, while significantly slower in portal vein (PV) area. Along with the collagen fibers deposition, the accumulation of triglycerides (TGs) was also significantly increased in CV area, but almost absent in PV area. Within the total TGs, although the amount of unsaturated TGs increased significantly in CV area, the lipid unsaturation degree, defined as the number of C=C bands on each fatty acid chain, significantly decreased in the late-stage compared to the early-stage fibrosis, which was likely due to the change of stearoyl-coenzyme A desaturase-1 (SCD1) expression. Meanwhile, lipofuscin and malondialdehyde, products of lipid peroxidation, were found to be gradually accumulated in CV area as fibrosis progressed. Interestingly, the heterogeneous distribution of collagen fibers and lipids were also found in fibrotic livers in patients. Our study by label-free multimodal NLO microscopy discovered the liver zonation of TG and collagen fiber, which could serve as valuable biomarkers for assessment of liver fibrosis progression.

Adipocytes are considered to be a critical cell type in the tumor microenvironment of breast cancer. Many studies have confirmed that adipocytes are not only found adjacent to cancer cells, but they also play an active role in the entire process of cancer development, progression, metastasis, and treatment response in breast cancer. Adipose tissue invasion (ATI) is a way of tumor cell metastasis, which not only indicates the poor prognosis of patients but also indicates the decline of survival rate. Multiphoton microscopy (MPM) with subcellular resolution based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) is very suitable for real-time detecting morphological and structural changes in biological tissues without tissue staining and exogenous probe molecule. In this study, MPM was applied to identify the adipose tissue invasion in breast cancer patients. The results indicated that it is feasible to detect adipose tissue invasion with multiphoton microscopy, and to provide a new auxiliary tool for pathologists to quickly and effectively diagnose adipose tissue invasion.

Coronary atherosclerotic heart disease is one of the main causes of death from cardiovascular diseases. Early detection of atherosclerotic lesions can help clinicians understand the condition of cardiovascular patients and provide reference for better treatment measures. Compared with other detection technologies, intravascular optical coherence tomography (IVOCT) has the advantages of no radiation, high resolution, and high imaging speed. Therefore, it plays an important role in the detection and evaluation of atherosclerotic plaque. Although IVOCT has been widely used in the detection of plaque in coronary vessels, the imaging system could not directly provide effective plaque feature identification information, and clinicians can only judge the characteristics of plaque according to their own experience. Based on a brief introduction of the application of IVOCT in detecting coronary atherosclerotic plaque, this paper introduces the method of eliminating the vascular motion artifact caused by cardiac pulsation. The automatic segmentation and classification of IVOCT images are studied by using machine learning method. And the plaque features of calcified plaque, lipid plaque and fibrous plaque in IVOCT images are extracted. The deep learning algorithm is used to analyze the characteristics of vulnerable plaque and put forward quantitative evaluation indicators. It is very important to develop the intelligent recognition system of IVOCT in plaque type, that provide objective, intuitive and accurate plaque classification marks, display and rupture risk assessment for the clinic. So that clinicians can get rid of the current situation of relying solely on experience for lesion recognition, and save patients' lives in time.

Optical coherence tomography (OCT) as an interferometric imaging technique, suffers from massive noise. Denoisingmethods are applied essentially to improve image quality in OCT community. The conventional methods rely onpost image processing algorithms such as non-local mean filtering, block-matching and 3D filtering algorithm. However, these conventional noise reduction methods could inevitably cause the destruction of image details, reduce the contrast at the edge of OCT images, and result in a degeneration of image quality. Current deep learning methods often ignorethespecificity of system, therefore haven’t taken advantages of the unique characteristics of different systems. In this work, we present a deep learning noise reduction method using the network architecture trained from synthetic OCTsignalswith random noise that are generated from the noise formation model characterized by our custom-built specificSD-OCT (Spectrum-Domain optical coherence tomography) system. We analyze the signal formation process and the noisegeneration pathway of our system, thereby enabling the construction of a noise formation model. DN-Unet (DenoisingUnity Network) is applied to train the datasets generated by our proposed noise formation model and the multi-to-singlestrategy is developed to enhance the network capability. Preliminary empirical results collectively showthat the networkcan reach an average of 25 dB signal to noise ratio (SNR) improvement while preserving detail structures, whichdemonstrates the effectiveness of our noise reduction method. This method has the potential to be adopted byother systems without the need for large number of golden-standard image generation.

In brain imaging, it is necessary to accurately distinguish between white matter, made up of axons and their myelin sheaths, and gray matter, made up of neurons and dendrites. Traditional optical coherence tomography (OCT) utilizes the scattering properties of light from the white and gray matter, however, the accuracy is restricted by the angle between the nerve fibers and the observation plane. In this work, we used polarization-sensitive optical coherence tomography (PSOCT) system to obtain the sample's birefringence information according to phase retardation image and proposed a two-parameter classification model containing reflectivity and birefringence, test the probability of white matter and gray matter using Bayesian detection methods, and adjust the threshold to obtain the best performance. The three-dimensional (3D) volume data were divided into 16 groups on average along the A-line direction. In each group, the white matter was set as 1 and the gray matter was set as 0, thus serving as the mask of the group. By applying multi-masks to 3D data, the position of white matter can be seen accurately. As a result, twice as much white matter was determined by proposed method than conventional method. The scattering and polarization information can be displayed simultaneously by encoding the HSV(Hue, Saturation, Value) model with reflectivity, phase retardation and optical axis orientation respectively. Our proposed two-parameter classification model can be used to distinguish white and gray matter in brain and spinal cord.

The critical investigation of mechanical stiffness of biological tissues is an effective investigative tool for the early detection of various diseases and delivers an impactful understanding of the physiological state of cellular to organ level. Elastography is an experimental technique that is used for investigating the qualitative and quantitative properties of the tissue. In the current work, we have explored the capability of optical elastography set-up for determining the mechanical properties of tissue-mimicking phantoms with varying stiffness. The tissue phantom surface was excited by an electromechanical actuator on various frequencies and phase-shifting algorithm is employed for reconstruction of the surface wave. The preliminary results obtained from the current work can be extended to investigate the in-vivo and ex-vivo tissues in biomedical applications for future clinical translation.

Nucleic acid detection is widely used in life science and clinical medical diagnosis. Innovative methods and platform research to improve its key performances are of great significance to ensure population health, promote precision medical technology, and even ensure social stability and development. Most of the existing nucleic acid detection technologies utilized PCR as the amplification method, relying on professional and complex scientific instruments and thus is time-consuming and laborious. Fortunately, RPA offers a feasible alternative. It has the advantages of fast amplification speed, high sensitivity, simple primer design, no temperature cycle control and complex manual operations. However, the detection of amplified products is difficult and costly, and there is a lack of low-cost real-time detection methods with parallel multiple detection abilities. In this work, a label-free and real-time RPA amplicon detection method based on hyperspectral interferometry is presented. A solid-phase biochip helps to capture the RPA product in a real-time meaner and the interference spectrum signal is used to read the solid thickness increment brought by the amplicon. A Fourier domain thickness computation method contributes to calculating the thickness increase and excluding scattering noise. The detection sensitivity reaches 6 copies/reaction and the consuming time is less than 20 min. Moreover, the detection method can also be used for single point mutation readout with the specificity of merelya1%mutation-wild type ratio. Combined with a microfluidic platform, parallel, simultaneous and multiple tests can be realized with 3 microliters.

Coherent Raman scattering spectroscopy provides a unique label-free, non-invasive approach for in situ interrogation of molecular-level information of specimen in a wide range of fields. However, it is often neglected that vibration is dynamic. Vibrational dephasing is susceptible to the nanoenvironment, and can be used as a unique probe for sensing complex intra- and inter-molecular interactions, especially in complex systems such as a living cell. By using a high dynamic range detection in time-resolved coherent Raman scattering (T-CRS) spectroscopy, we demonstrate vibrational dephasing time serves as a unique probe for sensing molecular interactions, which is in close connection to the macroscopic properties of specimen. Furthermore, such unique capability is promising for imaging applications of fields including biology, chemistry, and materials science.

The Spatial Frequency Domain Imaging (SFDI) is an emerging label-free imaging technique that can provide quantitative chromophore concentrations in tissue. It has been applied to various biomedical scenarios for the mapping of oxy-hemoglobin, deoxy-hemoglobin, water and lipids, including tumor imaging, inflammation monitoring, brown fat identification, blood lipids monitoring, and others. The key to accurate quantification of all four chromophores with near-infrared light is accurate optical absorption measurement at sufficient number of wavelengths, which has been a major challenge. To address this issue, we develop a hyperspectral SFDI system that is able to measure tissue optical properties in the entire 400 – 1000 nm range. With the proposed system, we’ll demonstrate quantitative mapping of tissue water and lipid content using NIR wavelengths rather than the shortwave infrared, which to the best of our knowledge, is unprecedented in the field of Spatial Frequency Domain Imaging.

As a biomarker for the diagnosis and treatment monitoring of various diseases, exosomes widely exist in body fluids such as blood, urine and saliva. However, its small particle size and low content are difficult to enrich. Therefore, a fast, high-purity enrichment method, and a fast, high-sensitivity, high-resolution, and low detection limit detection method are particularly important. We developed an automated fully integrated system for the enrichment and detection of exosomes. Using magnetic beads modified with anionic polymers to capture exosomes, adjust the pH of the exosome solution to acidic, and use the electrostatic adsorption between the positive charge on the surface of exosomes and the negative charge on the surface of the anionic polymer-modified substrate to achieve exosome capture. The captured extracellular vesicles are eluted from the surface of the magnetic beads by using a neutral or slightly alkaline eluent and using electrostatic repulsion to achieve the purpose of separation and enrichment of extracellular vesicles. The method is fast and efficient, can be automated with a small instrument, and can exclude favorable nucleic acid interferences. The eluted exosome protein is fixed on the substrate by chemical modification using quantitative interference exosome surface protein detection technology, and the connection between the exosome surface protein and the antibody is realized by immunoadsorption. Hyperspectral interferometry was used to quantitatively analyze the optical path increment on the substrate surface, to determine whether the exosome sample was bound to the antibody, and to detect the protein content of the exosome surface in parallel. This method can achieve sub-nanometer detection accuracy, and can detect exosomes whose size is smaller than the diffraction limit. Finally, the enrichment and detection of exosomes were automated.

Recently, optical methods have become one of the main tools for biomedical applications and have made significant progress in the field of clinical evaluation [1]. A number of natural tissues were studied using spectroscopic methods, including Raman spectroscopy [2, 3]. These vibrational spectroscopy methods are relatively simple, reproducible, do not destroy tissue, and require only small amounts of material (from micrograms to nanograms) with minimal sample preparation. In addition, these methods provide information at the molecular level, allowing the study of functional groups, bond types, and molecular conformations. Spectral bands in vibrational spectra depend on the molecule and provide direct information about the biochemical composition. These bands are relatively narrow, easily resolvable, and sensitive to molecular structure, conformation, and environment. A necessary part of the practical study of any sample is the theoretical prediction of the result in order to better understand the structure of the analyte. This part of the analysis is necessary for a more accurate selection of shooting parameters and a visual representation of the expected result. One of such prediction methods is mathematical modeling. This paper presents the results of studies of SERS spectroscopy of strains of mycobacterium tuberculosis strains and their comparison with theoretical Raman spectra of organic compounds, which were chosen as amino acids (L-histidine), tripedtide (glutathione), and carboxylic acid (citric acid).

A new method based on self-assembly and transfer printing was presented for plasmonic coloration. It could be adopted for not only different rigid substrates but also flexible substrates no matter the substrate is hydrophilic or not so that solvent-response plasmonic pattern could be fabricated. This method is low-cost, stable, and surface-universal to realize large-area plasmonic coloration.

The spectroscopic Single-Molecule Localization Microscopy (sSMLM) is an emerging tool offering exciting new capabilities for single-molecule localization and tracking. By simultaneously capturing the spatial location and full spectra of fluorescent emission from single molecules, sSMLM can significantly extend the number of distinct target species for multiplexed super-resolution imaging and offer desirable resolving power for functional super-resolution imaging. However, extracting accurate spectral information in sSMLM remains challenging due to the poor signal-to-noise ratio (SNR) of spectral images set by limited photon budget from single-molecule fluorescent emission and inherent electronic noise during the image acquisition using digital cameras. Here, we developed an unsupervised learning-based method consisting of a convolutional autoencoder and an unsupervised clustering algorithm for high-fidelity spectral denoising and high-accurate molecular discrimination. Compared to existing learning-based spectral classification methods used in sSMLM, highly accurate molecular discrimination can be achieved even at low photon budget while high-SNR spectra can be simultaneously recovered without undermining the fidelity of the spectral details. Since the developed data processing flow can be directly integrated into any current sSMLM hardware, we hope the unsupervised spectral denoising and classification method can facilitate highly accurate molecular discrimination for multiplexed super-resolution imaging and single-molecule tracking.

Photodynamic therapy (PDT) involves three interdependent parameters, including photosensitizer (PS), light, and oxygen (O₂). Obtaining an appropriate PDT dosimetry remains a highly challenging and labor-intensive work. In this paper, we described the design and assembly of a low-cost dual-wavelength illumination system for the in vitro PDT experimentation. A light-emitting diode (LED) array was designed to irradiate cells at 625 nm and 408 nm, respectively. An optical filtering layer with a printed pattern of five gradient pixel grey values was inserted between the LED panel and 96-well plate to achieve spatial light intensity control. The device was further integrated with an air-permeable aerogel monolith apparatus allowing for gas diffusion and delivery. The system performance was confirmed by testing the efficacy of 5-aminolevulinic acid (5-ALA)-assisted PDT on human breast cancer cell MCF-7. Cell viability results suggested that the high-energy violet light irradiation at 408 nm resulted in a more pronounced 5-ALA phototoxic effect at a much lower light dose. The gradient increase of the light dose for either red or violet light was followed by decreased cell viability. Regardless of irradiation conditions, the hypoxic environment significantly reduced the efficacy of 5-ALA-PDT. In conclusion, the illumination system can be used for reproducible and convenient in vitro PDT testing on the presentation of appropriate dosimetry of different light doses and wavelengths.

Intense pulsed light (IPL) is an incoherent high-intensity pulsed light and early studies suggest that it might be used in photodynamic therapy (PDT). This study investigated the feasibility of using IPL combined with Hemporfin, a porphyrin-type photosensitizer. The photodynamic effects of two IPL light sources (560-1200 nm and 640-1200 nm) were determined by photobleaching of Hemporfin and Hemporfin-mediated cell killing experiments. Results showed that when combined with Hemoporfin both IPLs could generate photodynamic effects. Therefore, it is feasible to use IPL as PDT light source.

The misuse of antibiotics has led to the emergence of drug-resistant strains of bacteria, increasing the difficulty and cost of medical treatment. Antibiotic susceptibility testing(AST) of bacteria can guide doctors and other relevant personnel to issue the appropriate amount of antibiotics, and traditional AST methods cannot give timely results. Although the existing rapid AST methods can reduce the testing time to less than 4 hours, they still have problems of high complexity and high cost. In this study, we achieved rapid AST by centrifuging the bacterial solution, rapidly settling the bacteria to the bottom of the counting plate, and then tracking the growth of the bacteria in a single field of view. Our method is consistent with the results obtained by the gold standard microbial broth dilution method for AST experiments and reduces the time to 4 hours. Compared to other rapid AST methods, our method reduces cost and complexity and is suitable for clinical and point-of-care applications.

With the improvement of people's health awareness, portable fundus cameras for health monitoring are popular. In order to solve the problem of non-mydriasis in fundus cameras based on new smart phones, we propose a dual channel lighting non-mydriasis method. The system focus with 680nm light, and captures color images with white light flash. In this study, a group of orthogonal polarizers were used to eliminate the stray light caused by the front eyepiece, and a circular light source was used to suppress corneal reflection. The system performs well with a field of view of 45° and a pupil diameter of 3mm, when refractive error ranges from -10D to +10D. The imaging quality of the system is good in 480- 700nm band. This study provides a reliable scheme for telemedicine of senile ophthalmic diseases.

Cavity ring-down or cavity enhanced expiratory gas diagnosis is a promising technology direction in medical diagnosis, which is expected to solve the problems of rapid, accurate, non-invasive and low-cost diagnosis of some diseases. The core of these techniques for breath gas diagnosis is to use the optical resonance’s enhancement effect to increase the interaction path between laser and breathing gas, so as to improve the detection sensitivity of breathing mark gas concentration. However, in the clinical application of this kind of technology, breathing gas needs to be filled with an ultra-low loss optical resonator. In this process, the pollution of optical resonator becomes an important bottleneck that affects and restricts the development of this technology. In this paper, different from the traditional way of gas pretreatment, the ideas to solve this problem are discussed and analyzed from the perspectives of pollution discovery, structure optimization, pollution immunity design, etc. Especially, focusing on the spectral ripple effect in the optical feedback cavity enhancement system, the optimization of V-shaped cavity structure and the aerodynamic design of cavity structure, the cavity pollution problem of V-shaped cavity is discussed and analyzed. These works hope to provide some references for accelerating the clinical application of this technology.

To study retinal hemodynamics in blood vessels of various sizes, from capillary to large arterioles and venules, in the living human eye, we developed a high-speed and high-resolution adaptive optics ophthalmoscope. This instrument employs a low coherent superluminescent near-infrared diode (λ=795 nm) as the imaging light source and a high-speed line camera to image the retina in a near-confocal mode through an anamorphic imaging mechanism. To image the erythrocyte flow in vessels with a diameter < 10 μm, retinal images were acquired with a full 2D raster scanning mode of 512 lines/frame. Blood velocity was measured by the spatiotemporal traces of the erythrocytes. To image the erythrocyte flow in vessels with a diameter < 10 μm, the scanner was programmed to stop across the vessel, thereby directly generating the spatiotemporal traces of the erythrocytes within the vessel in this frame. With the full 2D scanning mode over a field of view of 1.2°×1.2°, the ophthalmoscope produced retinal images with cellular-level resolution at a frame rate of 400 frames/second. This mode allows the erythrocytes flowing in the capillaries to be directly measured. With the partial 2D scanning mode, blood velocity in large retinal arterioles near the optic nerve head can be measured. The continuous velocity of the erythrocytes measured by high spatiotemporal resolution retinal imaging renders the fine profile of the erythrocyte movement.

It is known the disadvantages of assessing the content of advanced glycation end products (AGE) in the human body using autofluorescence of the skin. There are dependence on the skin phototypes, influence of hair, surface macrostructure and compression of biological tissues during investigation on AGE readers. On the other hand, the sclera of the eye is also composed of glycated collagen fibers and is available for in vivo fluorescence level estimation. This approach removes restrictions on the skin phototype and reduces the influence of other drawbacks. This study experimentally approves the opportunity of measuring the fluorescence of the human eye sclera in vivo for assessing the content of AGE in biological tissues. A correlation between the fluorescence of the skin of the hand and the sclera was established. The investigation of autofluorescence of the sclera and skin of the inner side of the forearm were carried out using a slit lamp. This lamp was equipped by authors with a semiconductor violet laser with a peak wavelength of 405 nm. This violet laser excites AGE fluorescence. A color camera was utilized as a photodetector. It was covered by a cutting violet light filter made of polyimide film 40 micrometer thick. The diagnostic parameter ("Sclera" or "SL skin") was the ratio of the arithmetic mean of two green pixels, one of which was exposed by violet laser, whereas another one was exposed by white light. The experiments involved 40 practically healthy subjects with ages from 18 till 70. The experimental results demonstrated an expected increase of diagnostic parameters within the age. The calculated values of the Pearson correlation coefficient between the diagnostic parameters "Sclera" / "Skin SL", "Sclera" / "Skin FL", "Skin SL" / "Skin FL" were 0.96; 0.95 and 0.99, respectively, indicating their significant statistical relationship.

Photoacoustic computed tomography (PACT), also known as optoacoustic or thermoacoustic tomography, is a rapidly emerging hybrid imaging modality that combines optical image contrast with ultrasound detection. Most currently available PACT image reconstruction algorithms are based on idealized imaging models that assume a lossless and acoustically homogeneous medium. However, in many applications of PACT, if the non-uniform acoustic properties of objects are not considered in the reconstruction algorithm, the reconstructed images may contain significant distortions and artifacts. This paper proposes a fully automatic double-SoS (Speed-of-sound) reconstruction algorithm which runs at 10Hzfortwo-dimensional images of (512)(512) pixels. The algorithm uses a deep learning model to segment the animal’s outer profile from the water background. We adaptively calculate the most appropriate sound speed and assign different sound speeds to the two regions for PA image reconstruction. The reconstructed images were compared to those reconstructed using a uniform SoS quantitatively.

With the advent of light-emitting diode (LED) increasingly used for Photoacoustic applications, it is imperative to mitigate the challenge of finding the most appropriate ultrasound transducer for a certain pulse excitation of the LED. The study aims to mitigate the challenge of finding the optimum operating frequency of the ultrasound required to detect the Photoacoustic signal while exciting the LED with a particular pulse input. The finite element method using COMSOL Multiphysics is implemented to perform a numerical model that fully couples electromagnetics, thermal, structural, mechanical, and acoustic physics. It is observed that as the pulse width of the LED excitation increases, the peak operating frequency of the ultrasound transducer also should increase. The operating frequency of the ultrasound transducer required for a particular pulse width of the excitation can be assessed using a finite element method approach.

Forces generated by cells are critical regulators of cell adhesion and function. Polymeric micropillar arrays have been widely used for measuring cellular traction forces, however, the sub-micron scale deflections of micropillars are typically measured with a high power microscope, leading to a limited field-of-view and reducing the measurement efficiency. Herein, we reports a typical 4F system used to monitor cell contraction force based on optical Fourier transform (OFT). Compared to conventional microscopy, with a field-of-view on the scale of millimeters and instant acquisition of the force map, this method makes high-throughput and real-time cell contraction monitoring possible. The cells growing on micropillar arrays were placed on the object plane of a typical 4F system, illuminated by a monochromatic plane wave. OFT was performed on the light field as it passed through the first Fourier lens. The spatial spectrum that consists of discrete light spots was projected on the back focal plane of the first Fourier lens and was then filtered by an optical stop, eliminating the extraneous frequency components while allowing the frequency spots corresponding to the period of the micropillar arrays to pass through the aperture. Inverse OFT was conducted as the filtered light passed through the second Fourier lens and an image was reconstructed on the image plane of the 4F system, which was recorded by a CCD camera with a 20X objective. The light intensity of the image directly represents the degree of periodicity of the micropillar arrays, in which low-intensity areas indicated that the micropillars in this area were deflected while high-intensity areas indicate the presence of uniform micropillar arrays. Using this method, cell contraction forces could be directly visualized based on the intensity distribution at the image plane with no need for further image post-processing, enabling direct visualization of cell contraction in a larger field-of-view.

In this manuscript, we aim to develop the dynamic light scattering imaging (DLSI) method to investigate the difference between triple negative breast cancer (TNBC) and human epidermal growth factor receptor-2 (HER-2) positive breast cancer. The experimental device with the capability to obtain time-series light scattering images was well designed and built. The breast cancer cell lines of MDA-MB-231 and HER-2 enriched SKBR3 were prepared and used for experiments. The autocorrelation functions of the light intensity fluctuation were calculated to characterize both types of breast cancer cells. The dynamic light scattering images were further analyzed to establish a DLSI-based approach for automatic classification of the two types of cells. The results show that the proposed DLSI-based model achieved better classification performance compared to the conventional static light scattering-based model.

Optical coherence tomography (OCT) enables non-invasive imaging of biological tissue and has become one of the most effective tools for monitoring the retinal structures and detecting retinal diseases. However, the existence of speckle noise severely degrades the OCT image quality and makes it difficult to identify the retinal disorders accurately. In this work, a deep generative model, named as despeckling generative adversarial network (DSGAN), is proposed for retinal OCT image despeckling. The proposed DSGAN is composed of two components, i.e., a despeckling generator and a discriminator. The despeckling generator employs the residual-in-residual dense block-based U-shape network to learn how to map the noisy image to the clean image. The discriminator learns to accurately discriminate whether the real clean images are relatively more realistic than the image generated by the generator. To improve the structure preservation ability during speckle noise reduction, the structural similarity index measure (SSIM) loss is introduced into the objective function of DSGAN to achieve more structural constraints. The proposed DSGAN was evaluated and analyzed on two public OCT datasets. The qualitative and quantitative comparison results show that the proposed DSGAN can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous despeckling methods.

Coagulation tests are essential for diagnosis of blood disorders and treatment of cardiovascular diseases. Here, we developed a noncontact method for quantitative assessment of coagulation properties based on blood diffusion dynamics analysis using optical coherence tomography (OCT). After a blood sample was loaded into a centrifuge tube, an OCT Mscan was captured at a single lateral location. Then the amplitude components of the complex OCT sequences were extracted for autocorrelation analysis. The length of the OCT sequence was optimized for the autocorrelation calculation. The blood dynamics related to scattering particle diffusion were evaluated by the autocorrelation decay over the time delay. The results show that the OCT autocorrelation analysis can quantitatively assess the blood coagulation properties without contact. Therefore, it could be used for in situ monitoring and point-of-care testing of blood coagulation with high reliability.

Speckle noise in optical coherence tomography (OCT) images seriously degrades the image quality and impairs the subsequent diagnosis of various ocular diseases. Most of the existing deep learning-based denoising models pay little attention to edge preservation, and rely on the large number of reference clean images which are hard to acquire in clinical OCT practice. In this work, an unsupervised retinal OCT image denoising model, named as edge-enhanced generative adversarial network (EEGAN), is proposed to free the dependence on reference clean images and enhance the edge information. Specifically, considering the noisy OCT image can be roughly divided into noisy retinal foreground and noise-only background regions, the generator of EEGAN is designed to denoise the noisy foreground samples based on the residual dense blocks, while the discriminator of EEGAN is employed to distinguish the real background noise samples from the fake noise samples, i.e., the difference images between the noisy foreground samples and its generated counterparts. As retinal edge details are the most vital information for disease diagnosis, an edge enhancement layer based on Sobel operators is integrated into the generator of EEGAN to strengthen the edge preservation ability of the model. Experimental results on clinical retinal OCT datasets show that our model has a better performance than the compared models in suppressing noise and preserving details, demonstrating the effectiveness of the proposed EEGAN.

Optical coherence elastography (OCE) can quantify the tissue elasticity by measuring the velocities of elastic wave propagation in the tissue. Due to the high sensitivity and micron-level resolution, OCE is especially suitable for biomechanical property measurements of the ocular tissues. Usually, the external excited elastic wave is visualized by optical coherence tomography (OCT). However, the imaging depth of the OCE system is limited by the OCT system and the excitation depth of external force. In this study, we proposed a method extending the OCE imaging depth with an electrically tunable lens (ETL). The method was validated by detecting the propagation of elastic waves in the corneas and retinas of porcine eyes using an acoustic radiation force-based OCE system. Firstly, an acoustic simulation was taken for the ring ultrasound transducer. Secondly, a mathematical model of the ETL was established for dynamic control of the imaging depth. Thirdly, the optical simulation of the sample arm was performed to analyze the critical optical parameters and evaluating the imaging quality of the system. Also, the optimal working depth of the OCT system was discussed. Lastly, an OCE system with a ring ultrasound transducer and an ETL was built. The experimental results on ex vivo porcine eyes showed the imaging depth of the system was 22 mm. This method can extend the depth of elasticity detection and, thus, provides a powerful tool for non-invasive, high-resolution biomechanical analysis of the ocular tissues.

The elastic properties can be an indicator of pathological changes of biological tissue. Acoustic radiation force optical coherence elastography (ARF-OCE) allows remote, non-invasive assessment of the elastic properties of tissue. In this study, we proposed an acousto-optic coupling ARF-OCE method for the elasticity measurements. The acousto-optic coupling unit employs a rectangular prism with a close refractive index but significantly different acoustic impedance compared to water. Therefore, the surface of the rectangular prism immersed in water can reflect the ultrasound beam while transmitting the optical coherence tomography (OCT) detection beam. We demonstrated the acousto-optic coupling ARFOCE method using agar phantoms. The results show that the ARF-OCE method can induce elastic vibrations in the direction parallel to the OCT beam, resulting in higher detection sensitivity and a larger scanning range.

At present, the imaging of fluorescence pharmacokinetic parameters based on dynamic diffuse fluorescence tomography (D-DFT) technology have limitations in some aspects including the accuracy of physical model and the quantification of method. In this work, we propose a fluorescence pharmacokinetic parametric imaging method of tumor tissues based on D-DFT and deep learning. It mainly includes: a more realistic training and test simulation data set can be established by combining non-uniform tissue photon transport model and biological tissue fluorescence kinetics method. A fluorescence pharmacokinetic parametric reconstruction algorithm that involves an improved U-Net architecture based on the fully convolutional neural network is developed to break through the complexity bottleneck of imaging physical model. The numerical simulation results show that the method can realize image reconstruction of pharmacokinetic parameters with high spatial resolution and high quantitative accuracy.

Test strips have been widely used in various rapid on-site biochemical sensing due to its simple and rapid detections. Unfortunately, many test strips only support qualitative sensing mainly because their detecting results are often determined by visual inspection. In order to solve the problem, we design a handheld fluorescence test strip reader, which can collect fluorescence emission from test strip thus quantify target under detection. The proposed handheld fluorescence test strip reader composes of rather compact optical system including a light emitting diode (LED) for excitation, an optical bandpass filter for fluorescence signal filtering and a smartphone for fluorescence signal collection and data analysis. All the optical elements are integrated using 3-D printed structures and coupled with the smartphone to support on-site detection. In addition, a smartphone application is also designed to automatically quantify fluorescence emission intensity, and it definitely can support quantitative target detection. Proved by a series of applications especially on rapid on-site detections, the handheld fluorescence test strip reader can detect target automatically, rapidly and accurately. Therefore, the proposed handheld fluorescence test strip reader is a potential tool for rapid on-site biochemical detection in various applications including agricultural, biomedical, and environmental fields.

In this work, the photoacoustic detection of blood glucose with the interference of multiple factors was studied. A set of photoacoustic detection system of blood glucose was established, in which the interference of multiple factors including the laser energy, concentration, temperature, flow velocity and detection distance were combined into. Under different conditions of multiple factors, the time-resolved photoacoustic signals and peak-to-peak values of blood samples were all obtained. To accurately classify the concentration of blood glucose samples, back propagation (BP) neural network was employed to train the photoacoustic peak-to-peak values and the multiple factors. In BP neural network, five different Arabic numerals from 1 to 5 were labeled to denote five kinds of blood glucose levels ranged from2mmol/Lto14mmol/L. The photoacoustic peak-to-peak values, laser energy, temperature, flow velocity and detection distance were used as the input data, the labels denoted different concentrations were used as the output data. Meanwhile, the effects of neurons number in hidden layer and learning factor on the classification accuracy of blood glucose level were investigated. Under the optimal parameters of BP neural network, the accuracy of classifying concentration of blood glucose level reached 85.6% for the test blood glucose samples. Compared with the classification accuracy (71.2%) of blood glucose level based on support vector machine (SVM) algorithm, it is demonstrated that the photoacoustic spectroscopy combined with BP neural network has a good performance in qualitative classification of blood glucose under the interference of multiple factors.

Optical coherence tomography-based angiography (OCTA) is a high-resolution imaging technology for mapping microvascular networks in vivo. Intensity variance OCTA methods have been developed for blood flow analysis. However, the complex statistical calculations of intensity variances result in high computational costs. In this study, we developed statistical algorithms for simplifying estimates of the intensity variances, including the range, the mean error, and the maximum error algorithms. A rat cerebral cortex was imaged by the simplified algorithms and the conventional algorithm. The number of repeated samplings was compared for the intensity variance analysis. Then the image quality and the calculation time were assessed. The results show that the simplified algorithms can shorten the calculation time and generate microvascular networks with similar image quality compared to the conventional intensity variance OCTA algorithm.

Ultrasound-modulated optical tomography (UOT) is a promising nondestructive technique for biological tissue imaging, and possesses both high sensitivity of optical imaging and good spatial resolution of ultrasonic imaging. In this paper, ultrasound-modulation of near infrared light is used to detect foreign bodies buried in turbid media. Experimental resultsshow that there is a linear increasing relationship between the modulation depth of acousto-optic signals andtheabsorption and scattering coefficients of turbid media. The position and shape of the foreign body (scatterer and absorber)buried in the tissue simulation sample can be reconstructed by the acousto-optic signals in UOT, and the strengthofscattering objects and absorption objects can be detected. But the scattering objects and absorption objects aren’t distinguishable in the imaging.

Multimodal imaging photoacoustic microscopy (PAM) and optical coherence tomography (OCT) can provide morphological and functional information of living organisms which reveals the physiological and pathological state of biological tissues. In this study, a dual-modal PAM and OCT system was custom-built to simultaneously image the optical absorption and scattering characteristic of rabbit eyes. Glycerol solution was used as optical clearing agent to enhance PAM and OCT imaging of New Zealand White rabbit eyes in vivo. Results showed that both OCT and PAM signals were increased by retro-bulbar injection of glycerol to New Zealand White rabbit eyes. The PAM signal amplitude increased from 0.1476 (a.u) to 0.1701 (a.u) after glycerol solution injection, which increased by 115%. And intensity of OCT signal was increased up to 150%. The optical clearing agent enhanced multimodal imaging technique provides a new method to increase the depth of optical measured depth and to improve the imaging contrast, which can be used to evaluate pathophysiological early symptoms in vivo.

Two dimensional (2D) light scattering technology is an important part of the single cell analysis, which can recognize the differences of inherent physical properties of biological particles. Based on the label-free static cytometry, collimated light from a laser with a wavelength of 532 nm is coupled into a multimode optical fiber. The fiber localizes and illuminates single static biological particles. Single particles are excited and their scattered light is collected. In this paper, the 2D light scattering patterns are obtained from 4 μm and 6 μm polystyrene microspheres (PS, refractive index, 1.59) and silica microspheres (SiO₂, refractive index, 1.45). The corresponding simulated scattering patterns are obtained by Mie scattering theory. Then, Fourier analysis of the 2D scattering patterns is performed for measuring the refractive index of single particles. The results indicate that the 2D light scattering technique may provide an efficient, low-cost, and easy-to-use approach to the nondestructive identification and classification of cancer cells at the single cell level.

Aiming at the problem that in microwave tumor ablation surgery, there is a lack of 2D curative effect evaluation method during and after clinical operations. This article discusses whether the infrared thermal imaging method can be used as an effective 2D evaluation method. This paper conducted two sets of experiments to validate whether the infrared thermal imaging method is effective and whether it can be used in real-time monitoring. In the experiments, microwave ablation system is used to conduct in vitro porcine liver ablation with different times and powers. In the first set, the size and shape of the ablation area are measured after the ablation time is completed, then actual ablation results, infrared thermal imaging results and simulation results are compared. In the second set, the infrared thermal imaging results are captured every minute during the ablation process, then compared with simulation results at each minute. Results show that the size and shape of the ablation area from the actual ablation experiments, the infrared thermal imaging method and the simulation are consistent, also the temperature change and the distinction of the different ablation zones are also consistent. In addition, second set experiment results are consistent with the intraoperative results. These results validate that the infrared thermal imaging method can be used as an effective real-time intraoperative and postoperative curative 2D evaluation method.

Phototherapy is beneficial in the treatment of many diseases, especially non-invasive phototherapy, because it directly irradiates the target tissue without irreversible damage. Phototherapy produces benign biological stimulation, responses, and photochemical effects that regulate a variety of bodily functions, such as blood function, nerve impulse transmission, enzyme activity, metabolism, and immunity. With the aging of the population and changes in lifestyle, the incidence of metabolism-related diseases is increasing. At present, the wavelengths used for the treatment of metabolism-related diseases in LLLT are divided into three wavelengths: red, near-infrared and ultraviolet. The most commonly used light source is a laser. However, with the development of third generation semiconductor materials and semiconductor lighting technology, a new light source, the light emitting diode (LED), has emerged. LEDs cover almost the full spectrum of wavelengths, which opens up the possibility of using phototherapy for different diseases. In summary, low light therapy is a promising method for the treatment of metabolism-related diseases. The development of safe, effective, low-cost, portable/wearable non-invasive phototherapy equipment is an important future direction for the application of low light therapy to the treatment of metabolism-related diseases.

The deformability of red blood cells is the main factor affecting blood flow and viscosity, and the study of red blood cells (RBCs) deformability is an important subject of hemorheology. At present, most of the measurement methods for red blood cell deformability are achieved by physical contact with cells or static measurement, and non-destructive high-throughput detection methods still need to be studied. A high throughput measurement method of human red blood cells deformability combined with optical tweezers technology and the microfluidic chip was proposed to accurately characterize the deformability of RBCs statistically. Firstly, the effective stretching deformation of RBCs was realized by the interaction of photo-trapping force and fluid viscous resistance. Secondly, the characteristic parameters before and after the deformation of the single cell were extracted through the image processing method to obtain the deformation index of area and circumference. Finally, statistical analysis was performed, and the average deformation index parameters were used to characterize the deformability of RBCs. A high-throughput detection system was built, and the optimal experimental conditions were obtained through a large number of experiments. Several groups of samples with different deformability were used for statistical verification. The results show that high-throughput detection and characterization methods can effectively distinguish different deformed RBCs statistically, which provides a solution for high-throughput deformation analysis of other types of samples.

Polarization imaging has shown the potential as a label-free tool in biomedical research due to its sensitivity to microstructures, especially the anisotropy analysis in pathological diagnosis. The digital pathology based on Mueller matrix systems has developed rapidly in recent years, and gradually progressed from qualitative to quantitative recognition. However, traditional critical illumination used in Mueller microscopes is likely to generate non-uniform projection accompanied by spatially varying irradiance, possibly due to unaligned optics, dust, nonuniform light sources, vignetting, etc. The non-uniform illumination results in artifacts in polarization images when stitching adjacent images to obtain a polarized image of the full sample, limiting the increase of the field of view (FOV). Therefore, a Mueller matrix microscope system with flat-field illumination and with motorized two-axis stage is proposed, we minimize the artifacts caused during splicing by uniform illumination, as well as increase the signal-to-noise (SNR) in the whole polarization images. The comparison of the real pathological sections under flat-field and wide-field illumination confirms the potential of large FOV imaging, which allows the structure of the sample to be presented more completely, laying the foundation for future pixel-level polarization digital pathology.

Artificial intelligence techniques have been deeply involved in the heterogeneous data aspects of biomedical applications. However, the high dimensionality and computational complexity of data can make classification, pattern recognition and data visualization difficult. Choosing appropriate dimensionality reduction techniques can help increase processing speed, reduce the time and effort required to extract valuable information, and ensure high accuracy. In this study, Alzheimer's disease data were taken as an example. Individual cases with missing values were removed, and non-digital data were converted to digital data using Min-Max normalization. Then principal component analysis (PCA) was applied to map the original feature space to 1 dimension and the variance of the validation set was calculated by 5-fold cross-validation to find the appropriate K value. The results showed that when PCA was applied to reduce the data to 1 dimension, the AUC (95% confidence interval) of the KNN classifier reached 0.898 ± 0.014, which was 30.4%higher than the case without PCA. Our current findings suggest that in many busy clinics and hospitals, it is quite worthwhile to use dimensionality reduction methods to save model computing time and to use KNN models to obtain better accuracy.

In this paper, a set of geometric parameters metrology system for contact lens based on SD-OCT (Spectral domain-optical coherence tomography) is developed and optimized, which can dealing with difficulties in measuring geometric parameters of contact lens, especially those with complex structures and surface shapes. Dispersion compensation, SNR(Signal-to-noise ratio) improvement and error compensation are introduced to improve the measurement accuracy. What’s more, the developed system is calibrated according to JJF 1148-2006. After calibration, the system meets the following indicators: test range of diameter: 8 mm-16 mm, indication error: ±50 μm; test range of center thickness: 0mm-1 mm, indication error: ±5 μm; test range of curvature radius: 6.5 mm-9.5 mm, indication error: ±20μm, repeatability of measurement: 10 μm. In addition, key parameters of a rigid contact lens with complex structure are measured, including diameter, vector height and center thickness, which can be recognized and measured automatically.

As a high-sensitivity and high-specificity imaging method, fluorescence molecular tomography (FMT) can quantitatively reconstruct the distribution of fluorescence sources inside the organism, and has great application prospects in tumor diagnosis, medicine development, and treatment evaluation. However, the reconstruction accuracy of traditional FMT is limited by the oversimplified forward model and the severe ill-posedness of the inverse problem. A physical model-driven iteratively unfolding network named ISTA-UNet is proposed in this paper. By combining the model-driven Iterative Shrinkage/Thresholding (IST) process and the UNet network model, the ISTA-Unet framework can take advantage of the denoising and detail recovery capabilities of deep neural networks on the basis of guaranteeing interpretability. In order to verify the effectiveness of the network, this paper analyzes the reconstruction of fluorescent targets with different positions, edge-to-edge distances, and fluorescence yield ratios. The results demonstrates that the FMT reconstruction based on ISTA-UNet has a significant improvement in spatial resolution and quantification compared with traditional methods, and has great potential in improving the quality of image reconstruction.

The non-contact tonometer transfer standard is an eye-like design that simulates the human eye's response to the airflow and light beam emitted by the non-contact tonometer. The rubber eye, one of the most commonly used transfer standards, is currently available in two structural designs: discrete and adjustable. In this paper, 4 kinds of rubber eyes made by different manufacturers were tested on 10 different models of non-contact tonometers. The results showed that except one model could not be tested with rubber eyes, the measurement values of the other 9 models of non-contact tonometers had large dispersion and low consistency, whether discrete or adjustable rubber eyes are used. Therefore, it is unscientific to assign a fixed reference pressure value to the transfer standard to verify various models of non-contact tonometers. The determination of the reference pressure value should be further studied to ensure the accuracy of the measurement results of the non-contact tonometer.

Droplet-based microfluidics has been demonstrated to offer the advantage of high precision control, high throughput, and material savings. Traditional fabrication methods of droplet generation involve soft lithography, molding, etching, and embossing process, relying on expensive equipment, tedious operations, and even requiring a cleanroom fabrication environment. Recently, 3D printing with the characteristics of low cost, easy reprinting, and well-achieved microstructure in three dimensions has been suggested as a promising technology to improve the fabrication of microfluidics. In this study, we combined 3D printing technology and microfluidics to design a microfluidic device that enables the continuous generation of droplets, which can be utilized to encapsulate single particles and cells. The digital model of the microfluidic device was designed and edited by software, and then uploaded to a stereolithography 3D printer with a resolution of 10 μm for printing. To verify the feasibility of the device to generate droplets, the mineral oil and water were used as the continuous phase and the dispersed phase, respectively. The diameters of droplets ranging from about 70 μm to 240 μm and the product rate about 1500/min can be achieved. The result of encapsulation probability of microspheres is around 55% with that of the single-microspheres about 30%, which verifies the ability of droplet device for encapsulating single particles. The droplet microfluidics is applied for cell imaging to monitor the cell viability for a long time. The result presents the viability changes from the living state to death of MDA-MB-231 breast cancer cells.

Laser-induced liquid plasma formation and plasma-induced ablation of thrombus will occur in laser atherectomy. In order to avoid damage to blood vessels and increase the ablation rate, it is necessary to conduct simulations and experiments for the interaction process. A spatial and temporal distribution numerical model of laser-induced liquid plasma formation was constructed, and the ionization process in the range of 600μm before and after beam waist was calculated. The plasma profile showed a crescent shape shifted to the laser incident direction. It was found that compared with water, the higher extinction coefficient of blood restricted the plasma area to the focus and optical axis, and the stronger absorption capacity of plasma made the plasma shift toward the laser incident direction. The simulation results can fit the experimental results in water and blood diluent.

Microplastics are ubiquitous pollutants in the living environment and food chains. Microplastic particles were recently even discovered in human blood for the first time. For high-throughput biodistribution analysis in toxicity studies, we propose a new deep-learning-based method for the automatic identification of polystyrene particles in whole blood. Attention-YOLO helps the network improve detection accuracy by adding the channel attention mechanism to the feature extraction network. We use microscopy to collect a dataset consisting of bright-field images of particles in various colors to train and test a neural network model. Then we use the model to identify and count polystyrene particles in untrained carp blood. The experimental results show that the Attention-YOLO network, when compared to the standard YOLO network, can achieve a better detection performance of microplastics counting in carp blood without adding too many extra parameters, with improvements in the recognition accuracy of polystyrene microspheres (ps-Bs, ps-Rs, and psGs) of 0.2%, 1.1%, and 6%, respectively, and the mean Average Precision (mAP) of 2.4%.

Extra virgin olive oil has very high nutritional value and its price is 5 to 6 times the price of ordinary cooking oil. The national standard detection of physical and chemical indicators of extra virgin olive oil requires very expensive chemical analysis instruments and related professionals, which is limited the market supervision of olive oil. Studying the threedimensional fluorescence spectra of extra virgin olive oil and other kinds edible vegetable oils revealed that the 3D fluorescence peak of extra virgin olive oil showed significant specificity with others. Three-dimensional fluorescence matrix data with full-wavelength scanning is large amounts of data, and the excitation fluorescence peak data was selected for olive oil quality algorithm study. Many oil samples were analyzed which include different brands of extra virgin olive oils, refined olive oil, and different proportions adulterated olive oil, and doped with peanut oil from 0.5% to 50%. When the fluorescence excitation wavelength was from 350~360nm, fluorescence data of three bands ranging were extracted for quality factor N, from 675~685nm, from 400~450nm, 500~525nm. N is has positive correlation with olive oil quality, resolution is 0.5%. The weighted normalized quality factor nonlinear curve fitting correlation coefficient for the quality factor based on the 3D fluorescence feature peaks reached 0 to 0.985, with a deviation of 2.63%, and the measured dynamic range was 49.6 dB. The quality factor identification method based on 3D fluorescence peak is no preprocessing and has high sensitivity. The algorithm only requires few fluorescence spectral data, which provides theoretical support for the low-cost and fast quantitative analysis of olive oil quality.

The degree and variants of structural changes in the vessels of the skin are important pathomorphological signs of the degree of tissue damage and potential possibilities and the course of its regeneration in various skin injuries, including burns. Objective: to identify arteries and veins with 3D optical coherence tomography during the experiment, describing their distinctive features. Materials and methods. To identify the vessels on OCT images of the skin, the rabbit ear was examined in vivo over the superficially located central artery and marginal vein and the vascular-free zone with a needle mark in the lumen of the vessel. Histo-tomographic comparisons were performed. Result. OCT signs of arterial and venous vessels are formulated.

Some pathological conditions accompanied by dynamic monitoring of the state of the pleura and subpleural lung tissue. For the most accurate diagnosis and selection of an adequate method of treatment, it is necessary to assess the morphofunctional state of the pleura and subpleural tissue, as well as monitoring the effectiveness of therapeutic and preventive measures, which necessitates intravital methods of their visualization. In case of hydro- and / or pneumothorax, the addition of puncture and drainage of the pleural cavity with optical coherence tomography of the pleura and subpleural lung tissue without lung injury makes it possible to diagnose thickening of the visceral pleura; cellularity is disturbed or absent, roundness or flattening of the shape of the alveolar cavities is revealed, up to the complete absence of visible alveolar cavities, the heterogeneity of their contents, the thickening of the interalveolar septa is determined initially and during treatment.

We tested high-frequency photoacoustic imaging for efficacy in recovering the histopathological structures from hematoxylin and eosin (H&E) stained tissue sections. The results showed that exogenous staining improved the absorption contrast of the histology microscope slides, allowing the detection of adipose tissue structure with a micrometer resolution throughout the tens of micrometer-square area range. The comparative spectroscopic study proved that photoacoustic imaging caused two simultaneously occurring processes: photodegradation of H&E (and formation of stable photoproducts absorbing in the blue spectral region), and ultrasound assisted organic stain removal from the microscope slide sample, reducing the exogenous contrast-distinguishable photoacoustic detection area.