This PDF file contains the front matter associated with SPIE Proceedings Volume 13271, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Benefiting from the high imaging resolution and deep penetration depth, Optical coherence tomography (OCT) is extensively applicable in ophthalmology, dermatology, and other clinical fields. However, the imaging quality is usually compromised by some noises such as horizontal coherence stripes, periodic background noise, and speckle noise. This paper proposes a multi-noise removal algorithm that combines spatial and transform-domain methods with optimized wavelet threshold denoising. This algorithm eliminates horizontal coherence stripes by generating a denoising mask through image segmentation and connected-domain filtering of superimposed B-scan images, utilizing the mask to remove these stripes. Besides, the periodic noise is removed by using frequency domain filters, while the speckle noise is also suppressed with the optimized wavelet threshold denoising method. We performed skin imaging using the SS-OCT system, processed the images, and evaluated the algorithm by quantifying the parameters such as signal-to-noise ratio, contrastto-noise ratio, and equivalent number of looks. Results demonstrate that the proposed algorithm can effectively suppress multiple noises while retaining the original detailed information. This study offers an ideal solution for OCT image denoising, potentially extending its clinical applications.

While the association between sperm selection with Hyaluronic Acid (HA) and DNA quality is well-accepted, the underlying mechanism remains unclear. In order to shed light on this issue, micro-Raman spectroscopy was utilized to analyze the differences in the Raman spectral response of sperm cells bound with or without HA. Our results demonstrate that the Raman spectra of HA-bound sperm and HA-unbound sperm display distinct differences in Raman spectral response. Furthermore, we conducted nucleoprotein and nuclear DNA fluorescent staining to validate our findings, demonstrating that HAbound sperm exhibit enhanced maturity in nucleoproteins and heightened DNA duplex quality. These findings substantiate the effectiveness of HA in selectively identifying highquality sperm cells. In summary, our research suggests that HA can be a valuable component for efficiently selecting high-quality sperm cells, and the integration of HA screening with micro-Raman spectroscopy presents a promising, non-invasive approach for objectively evaluating and screening strictly high-quality such cells.

Aiming at some common problems of the current dual-wavelength multi-step phase-shifting interferometric imaging method, a dual-wavelength generalized phase-shifting interferometric imaging method is proposed in this work. In this method, the phase shift can be any value within (0, ), which need not be a special value or a known value, and the introduced phase shift of two wavelengths is independent of each other. By calculating the difference between each two phase-shifted interferograms and the Hilbert transform values, the introduced phase shift can be calculated, and then the wrapped phase distribution at a single wavelength can be calculated, and finally the continuous phase under the synthetic wavelength can be obtained. Then through a series of simulation, the effectiveness and feasibility of this method are confirmed. Finally, it is concluded that proposed method can not only quickly process open straight stripes, but also process closed circular stripes, and have very high accuracy and excellent phase recovery effect.

The propagation of subthreshold signals is an important part of information exchange in neural systems, but the propagation mechanism of subthreshold signals in neural networks and the influence of internal and external factors on propagation are still unclear. Based on this, firstly, a mathematical model for the transmission of subthreshold excitatory postsynaptic current (EPSC) signals in a multi-layer feedforward neural network is constructed. Each element is a HH neuron model. To simulate the connections between neurons in different layers of the cerebral cortex more closely, a multifactor WS small-world network (MF-WS) is proposed, which can form inter-layer differences by adjusting multiple factors. Then, according to the influence factors of MF-WS small-world network formation, explore its influence on EPSC signal transmission. Through research, the increase of synaptic coupling strength W, the number of neighbors K and the proportion of excitatory neurons R are conducive to the propagation of EPSC signals. With the increase of the respective weights of multiple factors, the optimal noise intensity conducive to the propagation of EPSC signals decreases. Finally, a multifactor-consistent BP neural network (MF-C-BP) is proposed to optimize the synaptic coupling strength and connection mode between layers, which increases the consistency of the neural network pulse discharge sequence after the EPSC signal transmission to real correlation (± 0.30 - ± 0.50) or significant correlation (± 0.50 - ± 0.80). By studying the propagation mechanism of EPSC signal and the consistency of pulse discharge, a more potential mechanism for EPSC signal coding in neural network is provided.

In recent years, Mueller matrix polarimetry has demonstrated significant advantages in assisting clinical pathology diagnosis. However, to address the challenge of providing clinicians with intuitive understandings of the structural information for the samples in polarization imaging, it is often necessary to directly transform polarization images into standard pathological stained images for pathologists through virtual staining techniques. In this study, we propose a polarimetric virtual staining method based on Cycle-Consistent Generative Adversarial Networks (CycleGAN) that employs unpaired Mueller matrix polarimetric images and bright-field images to generate standard hematoxylin and eosin (H&E) stained tissue images. In comparison to existing techniques that are primarily based on paired image model training, the proposed method simplifies the process of data acquisition and preprocessing. This preliminary demonstration offers insight into the potential of polarization-assisted digital pathology in clinical applications.

Acute wound injury is a common type of sports injury, and its local pathological changes are mainly traumatic inflammation, cell proliferation and tissue repair process, microvascular contraction often occurs briefly, vascular permeability increases, and water, electrolyte and plasma protein penetrate into the tissue space. Low-dose PDT (photodynamic therapy) promotes wound healing by promoting fibroblast migration, inducing instantaneous in-situ reactive oxygen species, promoting epidermal stem cell proliferation and migration, promoting dermal granulation tissue formation and angiogenesis, and regulating inflammatory processes. The CAR peptide, previously identified, was utilized as an adhesion molecule to facilitate the formation of tight junctions (TJ) and regulate intercellular adhesion among adjacent epithelial cells. Additionally, it was incorporated into the photosensitizer 5-ALA, presenting a novel antibacterial repair strategy for sports injuries.

Fluorescence fluctuations super-resolution microscopy (FF-SRM) is a powerful tool in imaging and monitoring of biological subcellular structures and dynamics in cells. A variety of image reconstruction algorithms have been developed for FF-SRM. In order to obtain high spatiotemporal resolution, a U-Net-based deep learning method for super-resolution imaging of fluorescence fluctuations was developed. With just 20 frames, super-resolution images reconstructed using U-Net model could be comparable to those reconstructed using VeSRRF algorithm with several hundred frames, demonstrating its capability of advancing imaging capabilities.

Laser scanning measurement systems are capable of accurately obtaining three-dimensional surface information of objects, with wide measurement range, high precision, and fast measurement speed, which are widely used in biomedical fields such as dental mold fabrication and surgical navigation. The laser triangulation systems are one of the most commonly used laser scanning systems. In laser triangulation systems, the processing speed of laser stripe images is crucial for determining the system's measurement efficiency. This paper focuses on real-time processing methods for laser stripe images in laser triangulation systems, developing a fast and accurate processing method based on the Zynq MPSoC platform. Gaussian filtering was first applied to the laser stripe images, followed by the calculation of eigenvalues and eigenvectors of the Hessian matrix. Subsequently, the rapid and accurate calculation of the laser stripe center position was completed. Experiments verified the accuracy and speed of the FPGA-based image processing algorithm proposed in this paper. Results showed that the accuracy of the FPGA-based algorithm is consistent with that of the traditional OpenCV-based algorithm, with measurement time reduced to 1/18 of that on the software platform. This significantly improves the speed of laser stripe center extraction without compromising image processing accuracy. The FPGA-based laser stripe center extraction algorithm developed in this study is valuable for enhancing the measurement speed of laser triangulation systems and offers significant insights for high-precision, fast image processing technologies in related fields.

Structured illumination microscopy (SIM), as one of the mainstream super-resolution optical microscopy imaging techniques, takes the advantages of fast imaging because of wide-field illumination, which is suitable to the multi-color live cell imaging. However, multi-color imaging measures each spectral image separately, so the imaging speed of is limited. In addition, the spatial-resolution improvement of SIM depends on the modulation frequency of structured pattern. To further improve the imaging resolution, we use a spectral imaging method, i.e., passive ghost imaging, in SIM. Simulation results show that the proposed method has superiority in super-resolution, imaging speed and imaging quality than traditional multi-color imaging and multi-color SIM imaging.

This paper presents a system for detecting orange juice concentration based on the principle of solution absorption of visible light. The system efficiently detects orange juice concentration using visible light communication technology. It utilizes a LED to emit modulated light signals at specific frequencies. By leveraging the differences in absorption and scattering characteristics of orange juice solutions at varying concentrations, the system can determine the concentration of the juice. It features low cost, high sensitivity, and strong anti-interference capabilities, offering significant application value. The system primarily consists of an Arbitrary Waveform Generator (AWG), LED light source, lamp cover, silicon photo diode (PD), and oscilloscope. After passing through the orange juice solution, the specific frequency light signals are received by the silicon photo diode and converted into electrical signals. The oscilloscope measures the peak-to-peak data of the light signals, translating it into signal power levels to obtain the concentration information of the orange juice solution. The raw data are fitted with linear and quadratic functions, yielding R 2 values of 0.91547 and 0.99546, respectively. This system establishes a correlation between the concentration of orange juice and the signal power level converted from received light, based on the absorption and scattering characteristics of the juice solution at specific frequencies. Experimental results demonstrate that the system can accurately detect different concentrations of orange juice solution, showing good linear fit and broad application prospects, suitable for quality monitoring and real-time detection in orange juice production processes.

Surface-enhanced Raman spectroscopy (SERS) is a molecule-specific spectroscopic technique known for its high sensitivity, rapid response, and non-destructive testing capabilities. The physical structure of the SERS substrate has the most significant impact on the enhancement effect, which has nanoscale roughness or specific metal nanostructures, such as metal nanoparticles, nanowires, or nanorods. Upon exposure to laser irradiation, nanostructures induce an intensified electromagnetic field, namely localized surface plasmon resonance (LSPR), thereby augmenting the Raman scattering signal. Furthermore, the spatial arrangement of metal nanostructures similarly influences the LSPR. In this study, we designed a three-dimensional porous SERS substrate that has a larger surface area than traditional planar substrates, adsorbing more silver nanoparticles (Ag NPs). We utilize the aggregation effect of Ag NPs to induce an enhancement of the electromagnetic field, thereby amplifying the SERS signal. To validate the efficacy of the porous SERS substrate, this study employs Comsol simulation software to compare the electromagnetic field intensity between the porous substrate and conventional planar structures. Simulation results demostrate that the porous structure achieves a stronger electric field intensity under the same laser irradiation, thus enhancing the SERS signal intensity.

The endoplasmic reticulum (ER) is an organelle consisting of a network of membranous structures essential for various cellular activities, making its homeostasis critical for proper cell function. The composition of its membrane can be easily affected by various cellular stressors, triggering ER stress response. Therefore, conducting a detailed structural and compositional analysis of ER is crucial. However, due to resolution limits, analyzing the ER composition in situ remains difficult. Here, we propose a dual-modality imaging and analysis method integrating stimulated Raman scattering (SRS) and structured illumination microscopy (SIM) for imaging the lipid and protein contents of ER structures. With super-resolved structural guidance provided by SIM, the protein/lipid ratio was quantified for ER using multispectral SRS imaging. The spatial mapping of ER compositions in a single cell revealed subcellular diversity in the protein and lipid ratios in the ER structures, which significantly altered under ER stress.

The combination of optogenetic modulation and electrical neural recording allows for the interrogation of neural circuits with millisecond temporal resolution and cell type specificity, offering promising prospects for fundamental neuroscience research. However, the mechanical stress exerted by conventional rigid neural probes on surrounding tissues postimplantation can elicit the inflammatory response or neuronal death, thereby compromising their long-term performance. In this study, we propose a novel ultra-flexible polymer-based neural optoelectrodes design that is monolithically manufactured using planar semiconductor fabrication technology. Furthermore, we characterize its optical propagation and in vivo neural recording performance. Compared to previous approaches, our innovative optoelectrodes design offers improved flexibility and biocompatibility, providing unparalleled versatility for investigating neural circuits with high spatiotemporal precision. Our work paves the way for stable long-term multimodal neural interfacing applications in the future.

Small extracellular vesicles (sEVs) are nanoscale bioparticles released from various cells and have important applications in clinical and basic science. In nanoscale particle tracking, the tracking trajectory length is important for the accurate sizing of nanoparticles (NP). Here, a light scattering imaging system uses a 786.4 nm laser source to collect the side scatter of individual nanoscale particles with a 10X objective lens and a CMOS camera is introduced. Supervised sliding window analysis is tested for optimized NP trajectory segmentation, followed by a machine learning algorithm that classifies Brownian motion and non-Brownian diffusions based on tracked trajectory features. Supervised sliding window analysis allows the differentiation of non-Browian diffusions with a high accuracy of 93.8% and precise sizing of standard polystyrene NPs. Imaging and size measurements of 120 nm NPs, 65 nm NPs, and plasma-derived sEVs show that optimizing the trajectory length combined with purifying the non-Brownian diffusion improves the sizing accuracy. Nanoscale EVs are expected to be reliable biomarkers for many diseases, especially those associated with cancer, where reliable and accurate size estimation methods based on light scattering imaging have potential applications.

Light scattering techniques are highly sensitive to structural changes in live and fixed cells, enabling label-free singlecell analysis compared to traditional fluorescence techniques. Solving the inverse problem of light scattering is interesting, as it provides the physical information from label-free single-cells. In this paper, a 532 nm laser is focused to excite a single cell to acquire light scattering images. A 10X objective lens and a CMOS camera are used to detect the elastic scattering perpendicular to the laser excitation direction. Yeast cells are cultured in a liquid containing 2% glucose and incubated at room temperature. Cell samples were obtained on day 1 and day 4, and their light scattering images were acquired, respectively. By solving the inverse light scattering problem, we found that the refractive index of yeast cells increased on day 4 of incubation compared with day 1. By combining machine learning technology with light scattering imaging, the average classification accuracy of label-free yeast cells on day 1 and day 4 is 98.3%. These findings prove the high sensitivity of light scattering imaging to the physical variations in single cells and speculate the possible applications of this technology in label-free live cell analysis.