This PDF file contains the front matter associated with SPIE Proceedings Volume 12144, including the Title Page, Copyright information, Table of Contents, and Committee Page.

A microfabricated fiber-based optrode is presented, which performs simultaneous light delivery and electrophysiological recording. The fabrication approach assures minimal tissue damage during insertion. The device consists of a tapered fiber tip and three separate metal electrodes deposited on the cylindrical surface of the optical fiber. The tapered fiber tip enables precise light emission, as well as reliable light delivery to the targeted volume of brain tissue. Our custom precision fiber lateral and angular alignment setup allows us to fabricate a novel fiber based optrode with three separate equidistant metal electrodes along the fiber up to the fiber tip. Here we present our fabrication approach.

The customized 3D illumination patterns can be generated with computer-generated holography (CGH), and the axial confinement of the illumination patterns can be improved by inducing the temporal focusing technique. Through these approaches, the neuron excitation in single-cell resolution can be achieved. However, due to the computation cost of iterative CGH algorithm, the hologram must be pre-calculated to generate the illumination patterns for neuron excitation. This shortcoming makes it difficult to dynamically stimulate the neurons for observing neural activity. To overcome this issue for real-time dynamic neuron stimulation, we develop a neuron stimulation system with single-cell resolution and a real-time CGH algorithm. For single-cell resolution, a diffraction grating is used to generate the temporal focusing effect. Moreover, we design a deep-learning based CGH algorithm considering temporal focusing effect to real-time generate hologram with the pre-trained U-net architecture for producing customized illumination patterns in 3D positions. In our approach, the dynamic 3D micro-patterned single-cell neural excitation can be achieved by inducing temporal focusing technique to improve the axial resolution to few microns level and generating hologram by deep-learning based CGH considering temporal focusing to speed up the computation time to tens of milliseconds.

Raman spectroscopy is evolving as a powerful optical tool for tissue discrimination in cancer diagnosis. In addition to diagnosis during biopsy and intraoperative procedures, there is also a compelling need to gain insight on the tumor location and geometry like depth, thickness, and size of subsurface soft tissue tumors for diagnosis and monitoring/surgery or treatment-related decision making. Spatially offset Raman spectroscopy (SORS) offers excellent potential in extracting the depth of the tumor by selectively collecting the photons that have traveled a longer path length using source-detector separation. The current work utilizes the property of SORS to estimate the varying size of an ellipsoid tumor resembling a flat adenoma located at various subsurface depths and tumor thicknesses. The prediction model is based on the continuous incremental movement of the probe position on the surface to analyze the change in relative tumor contribution calculated for the Raman scattered signal. Regression on Monte Carlo (MC) simulated SORS signals predicted the tumor dimension with a coefficient of determination of 0.94 and root mean square error less than 2%. This prediction model will help design probes for optimized application-specific SORS signal extraction and processing.

Despite its ultrasensitive detection capability, surface-enhanced Raman spectroscopy (SERS) faces challenges as a quantitative biochemical analysis due to the significant dependence of local field enhancement on nanoscale geometric variations. Significant efforts have been devoted to develop SERS calibration methods by introducing Raman tags as internal standards. Raman tags undergo similar SERS enhancement, and ratiometric signals for target analytes can be generated with reduced SERS enhancement variations. However, using Raman tags still faces challenges for label-free applications, including spatial competition between the analyte and tags in hotspots, spectral interference, limited long-term stability due to laser-induced photo-degradation. We demonstrate that electronic Raman scattering (ERS) signals from metallic nanostructures at hotspots can serve as the internal calibration standard to enable quantitative SERS analysis and improve biostatistical analysis. ERS is omnipresent in any plasmonic construct and shown as a broad continuous background in SERS measurements. Both ERS and SERS processes experience the |E|4 local enhancements during the excitation and inelastic scattering transitions. We demonstrate that ERS-calibrated SERS signals are insensitive to variations from different hotspots and thus can enable more accurate quantitative SERS analysis. For validation, we performed label-free SERS analysis of living biological systems using four different cancer cell lines cultured on SERS devices and their drug responses. Remarkably, after ERS calibration, the statistical scatter plots are more similar to the intrinsic biological properties of cancer subtype categorization and their known drug responses. Therefore, we envision that ERS calibrated SERS can find crucial opportunities in label-free molecular profiling of complicated biological systems.

T cell differentiation has warranted intense study to understand the mechanisms behind the adaptive immune system. While much of the research so far has relied on antibody staining and flow cytometry separation to isolate and study T cells, we present hyperspectral stimulated Raman scattering (SRS) microscopy as a potential label-free imaging method to directly observe and characterize T cells. We show that a deep learning model can be trained to identify and classify T cell differentiations from hyperspectral SRS images with 99% accuracy. We also show that fluorescent T cells in lymph node tissue can be predicted from SRS images, demonstrating potential towards an entirely label-free in-situ imaging strategy. SRS microscopy augmented with deep learning shows strong promise towards label-free in situ observation of T Cells.

Cameras with extreme speeds are enabling technologies in both fundamental and applied sciences. However, existing ultrafast cameras are incapable of coping with extended three-dimensional scenes. To address this unmet need, we developed a new category of computational ultrafast imaging technique, light field tomography (LIFT), which can perform three-dimensional snapshot transient (time-resolved) imaging at an unprecedented frame rate with full-fledged light field imaging capabilities including depth retrieval, post-capture refocusing, and extended depth of field. We demonstrated the proof of concept through light-in-flight imaging of a helical-shaped diffused fiber. The advantage of such recordings is that even visually simple systems can be scientifically interesting when they are captured three-dimensionally at such a high speed. The ability to film the propagation of light through a curved optical path, for example, could inform the design of invisibility cloaks and other optical metamaterials.

Here, we report a new handheld endoscopic system for nonlinear multimodal imaging of the head and neck region. It has a long rigid endomicroscopic probe with two versions (0° and 45° bended tip), connecting with a compact scan-head of approx. 10×12×6 cm³ size. The rigid probe is 6 mm in diameter and 24 cm long and allows diffraction-limited multiphoton imaging of tissue with at least 430 μm field of view and sub-micron resolution. The signals of Coherent anti-Stokes Raman Scattering (CARS), second harmonic generation (SHG), and two-photon excited fluorescence (TPEF) are collected by a non-descanned detection path in the scan-head, and the fluorescence of Indocyanine green (ICG) labeled lesions is detected by a confocal descanned configuration. Furthermore, this system is capable of guiding high-power femtosecond laser pulses for tissue ablation without the risk of damaging optical glass components.

A systematic investigation of the expansion dynamics of plasma plumes generated by two laser beams operating on homogenous and heterogeneous targets was undertaken using a technique involving fast-gated intensified charge-coupled device imaging.

Our experiments present the results on the temporal, spatial and semi-spectrally imaging of colliding plasmas of Aluminum and Silicon targets. The aim of the work presented here is to further advance and study colliding plasma techniques, as well as other methods to realize and control species density and expansion, with a view to a deep understanding of these complex mechanisms and optimize emission in the visible wavelength range. The analysis is focused on describing the velocity of the expanding plasma front for the interaction zone where the present results found the expansion velocity of the stagnation layer increases with time, also the fact that the laser energy reduces the velocity.

All investigations focus on studies of colliding laser-produced plasmas (CLPP) characterizations formed on wedge-shaped targets where the angle of the wedge varies from 180^o to 80^o. Time-resolved emission imaging was employed to track the size, shape and velocity of the stagnation layer which might act as a signature of hard versus soft stagnation. Moreover, this work investigated the effect on the homogeneity of the stagnation layer with the target angle.

The analysis suggests that there is significant collisional reheating of the stagnation layer followed by radiative recombination as well as this study provides a considerable amount of detailed data related to expansion velocity of the interaction zone which extends the Colliding plasma systems understanding of the behavior of species within CLPP.

Non-invasive methods of tracking morphological cell changes are based on measurements of phase, which is proportional to the cell thickness and allows calculation of cell volume. Additionally, Raman micro-spectroscopy is widely used for the mapping of chemical composition within live biological samples, such as cells, organoids, and tissues. We have previously reported the use of Raman spectroscopy and Digital Holographic microscopy (DHM) to study cell death induced by methamphetamine treatment. Here, we have replaced DHM with another method that is capable of real-time high resolution phase reconstruction. Assembling or altering a system to make the measurements required to solve the Transport-of-Intensity Equation (TIE) is easier than implementing a DHM setup. For the full phase retrieval, TIE requires only the data collected in the focal plane and in two planes symmetrically positioned about the focus. Furthermore, TIE is robust to reduced spatial and temporal coherence. Since TIE can utilize incoherent sources of illumination, we implemented a TIE setup within an existing Raman microscope, which provided near simultaneous chemical composition and morphological cell data. This setup is well-suited to study another form of programmed cell death, ferroptosis, which is the main cause of tissue damage driven by iron overload and lipid peroxidation. Previously, only invasive cell biological assays were used to monitor the expression level and subcellular location of proteins known to bind iron or be involved in ferroptosis. In this work, our group applied Raman spectroscopic techniques to study MDA-MB-231 breast cancer cells treated with an activator and/or inhibitor of ferroptosis.

Our previous studies have shown that it is possible to determine the growth activity of a microbial colony by laser speckle imaging techniques. A sub-pixel correlation method was proposed to detect small changes in the sequence of laser speckle images. In this study, we compared the laser speckle imaging method to the reference method - image time series under white light to detect the colony growth parameters (growth rate, critical detection time).

We are presenting the application of an optical and computational pipeline FAMOUS for revealing the presence of free viral particles named “virions”. The idea of such a protocol is to give rise to images of virions in their environment with a soft solution for recording the native image, contrary to the standard solution of imaging virions with electron microscopy (EM) for visualizing viral particles. The final aim of the current work is to observe free viral particles of SARS-CoV-2, the virions responsible for the worldwide pandemic of Covid-19. But such particles have diameters between 80 and 120 nm, a dimension smaller than the resolution limit of optical-only microscopy solutions. We have chosen to start with the biggest free virions, cytomegalovirus (CMV), a virus from the herpesvirus family also named “Human Herpes Virus 5”. Two kinds of cultures were involved: a fluorescent culture (BAD) and a label-free one (VHLE), both being collected from infected cell culture. VHLE virions were first observed after secondary immunostaining and concentrated with magnetic nanoparticles and then without labelling. The optical protocol rests on a standard solution of multiphoton microscopy combined with a computational strategy based on the point -spread-function (PSF) recordings, its mathematical modeling and the restauration of the image resting on the PSF model. A test with free viral particles of SARS-CoV-2 is led, delivering an optical visualization of the free-viral particles. The visualization of objects aggregates obtained in both situations confirm the relevance of the pipeline FAMOUS for imaging free virions.

The current protocols for biocompatibility assessment of biomaterials, based on histopathology, require the sacrifice of a huge number of laboratory animals with an unsustainable ethical burden and remarkable cost. Intravital microscopy techniques can be used to study implantation outcomes in real time though with limited capabilities of quantification in longitudinal studies, mainly restricted by the light penetration and the spatial resolution in deep tissues. We present the outline and first tests of a novel chip which aims to enable longitudinal studies of the reaction to the biomaterial implant. The chip is composed of a regular reference microstructure fabricated via two-photon polymerization in the SZ2080 resist. The geometrical design and the planar raster spacing largely determine the mechanical and spectroscopic features of the microstructures. The development, in-vitro characterization and in vivo validation of the Microatlas is performed in living chicken embryos by fluorescence microscopy 3 and 4 days after the implant; the quantification of cell infiltration inside the Microatlas demonstrates its potential as novel scaffold for tissue regeneration.

The recent development of the modern optical-electronic, especially spectroscopic technologies opened the possibilities of multispectral processing image processing for the analysis of the state of different biological objects. The human skin is among such objects. Fast foundation and diagnostics the skin lesions are the very actual problem of the modern medicine. We have proposed to realize the skin lesion sub-images multispectral processing method. Our approach means the conversion of the initial polychromic image into the series of the monochromic sub-images, each of which represents the light intensity distribution at one of the selected wavelengths. Hence, the bigger number of the wavelengths we use, the larger amount of the spectral information can be obtained regarding the studied bio-object. The acousto-optic tunable filters have been proposed to use as selective elements for multispectral processing. The experimental installation has been developed, with the software-controlled light source on the basis of the LED set. The functional circuit of the installation is represented as well as the results of the experiments carried out at it. For example, the experiments of the hand skin areas images multispectral processing at different persons in the light spectral range of 450…815 nm. The experiments results confirmed the operation ability of the installation as well as possibility to form the necessary set of the studied skin areas sub-images.

The imaging speed of Temporal focusing multiphoton excitation microscopy (TFMPEM) is up to hundreds frames rate. However, the plane illumination manner suffers from the sever scattering of biotissue and signal crosstalk that blurs the image. And the deeper the worse. Nevertheless, the high acquisition rate decreases the effective excited fluorescent, which reduces the signal-to-noise ratio (SNR) of the image. In order to solve the scattering and low SNR issues, the deep learning method is proposed to restore the TFMPEM image. In this work, we construct a powerful neuron network which called multi-stage 3D U-Net. Different from the cascade method, it becomes more connection between each U-Net. The previous stage information can share with the next stage, instead of seeing as independent. Thus, we try to restore the TFMPEM via this network with Point scanning multiphoton excitation microscopy (PSMPEM) image as the ground truth. But before that way, our two systems are not sharing the same optical path architecture, it needs to do the registration first. For cross modality registration, we utilize Voxelmorph which is also a 3D U-Net architecture. And it can do the not only global but also local deformation, is flexible than classical algorithm. Hence, we do the registration and restoration via all deep learning method. Therefore, the peak signal-to-noise ratio (PSNR) of the image can be improved around 20 to 30 dB and, and structural similarity (SSIM) is close to 0.9

A sonographic short cervix is a major risk factor for spontaneous preterm birth (PTB). However, the cervical length is a suboptimal means to assess cervical status due to the lack of functional and molecular information. Spectroscopic photoacoustic (sPA) imaging is a non-invasive ultrasound-based technology for assessing cervical tissue compositions, such as collagen-to-water ratio (CWR), which are the major molecular changes during cervical ripening. A longitudinal CWR measurement by sPA was performed in murine cervices (n=3 per group) through the gestational ages from nonpregnant, 13.5 to 19.5 dpc, 6 to 12 hours, and 69 to 94 hours postpartum. The sPA data acquisition was performed in a range of wavelengths covering the peak absorption of collagen and water (1070to 1650 nm) with an amplified sPA wavelength unmixing method (sPA-CWR). The results indicated that the sPA-CWR method is capable of accurately quantifying cervical tissue composition changes during cervical remodeling. The non-pregnant murine cervical samples have significantly higher sPA-CWR than any other tissue group. A decrement in CWR at larger gestational ages was detected, which follows the cervical ripening process. In addition, the repair process was detected through increased CWR in tissue samples collected 6 to12 hours postpartum and completing their recovering process at about 69 to 94 hours postpartum. Finally, the imaging results were validated by quantitative histological analysis. These histological results confirm that the sPA-CWR measurements have a high correlation to the process of collagen reorganizing. Therefore, the sPA-CWR method can be a more accurate biomarker for estimating PTB risk.

The mechanisms involved in neural tube formation are complex and can be easily disrupted. Neurulation is one such process, governed by mechanical forces where tissues physically fold and fuse. When neural tube folding and closure fail to complete during neurulation, it results in structural and functional abnormalities of the brain and spinal cord. Thus, it is important to understand the interplay between forces and tissue stiffness during neurulation. Brillouin microscopy is an all-optical, noninvasive, high-resolution imaging technique capable of mapping tissue stiffness, but it cannot provide structural information, resulting in “blind” imaging. To overcome this limitation, we have combined a Brillouin microscopy system with optical coherence tomography (OCT) in one synchronized and co-aligned instrument to provide structural guidance when mapping the biomechanical properties of neural tube formation in mouse embryos. We developed custom instrumentation control software that utilizes the OCT structural image to guide Brillouin imaging. We acquired first 3D OCT images and then 2D structural and mechanical maps of mouse embryos at embryonic day (E) 8.5, 9.5, and 10.5. Brillouin microscopy showed the cell-dense layer of neural plate derived from the ectoderm at E 8.5, which was unable to be distinguished with OCT. At E 9.5 and 10.5, the neuroepithelium could be clearly seen by Brillouin microscopy with a greater stiffness than the surrounding tissue. Our results show the capability of the co-aligned and synchronized Brillouin-OCT system to map tissue stiffness of murine embryos using OCT-guided Brillouin microscopy.

Despite recent outstanding development, machine learning (ML) has not been utilized in mid-infrared and photoacoustic spectroscopy for noninvasive glucose detection. ML models can assist in improving the detection sensitivity to meet FDA requirements. Furthermore, the employment of ML can help to solve the complexity of detecting glucose in the presence of different blood components or at various environmental conditions. In noninvasive optical spectroscopy, ML models can be developed to distinguish glucose signals despite the variations in human skin properties for in vivo measurements. Different ML classification algorithms have been developed and employed to detect glucose levels using MIR-infrared photoacoustic spectroscopy. The photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm⁻¹ for noninvasive glucose monitoring. Artificial skin phantoms have been prepared as test models for the system with different glucose concentrations, covering the normal and hyperglycemia blood glucose ranges. Support vector machine, narrow neural network, and medium neural network algorithms have achieved high prediction accuracy in classifying glucose levels.

Discriminating type I and type II collagen is important owing to its dominating presence in cartilage and connective tissues where an alteration of collagen matrix is observed in several diseases including osteogenesis imperfecta and osteoarthritis. For non-destructive investigation of the molecular level properties of collagen, a non-invasive Dual-liquid crystal based polarization-resolved second harmonic (SHG) microscopy is utilized to facilitate the quantitative characterization of collagen types I and II in fracture healing tissues. In this study, we extend an existing approach allowing the quick generation of any desired linear polarization states without any mechanical parts to quantify the characteristics of collagen types using pitch angle and anisotropy parameter. Furthermore, data reliability is ensured by using right and left-hand circular polarization imaging centered circular dichroism analysis. Our findings indicate that the effective pitch angle for the collagen at fracture healing tissue is 48.4° and 49.9° at two weeks and four weeks of repair respectively where type II collagen dominates in the former and type I in the latter. The mean SHG-CD response of the articular cartilage is 0.271 and 0.183 at the rich zone of collagen types II and I, respectively. These findings are correlated to the values obtained from the non-fractured control bone tissue. The measurements obtained reflect the different types of collagen in the molecular fibril assembly. Therefore, these methods demonstrate a powerful tool to provide new insights on understanding the role of collagen in ECM structure and on the development of cartilage repair.

In this work we present experimental demonstration of focal-field engineering in infrared-sensitive third-order sum frequency generation (TSFG) microscopy by utilizing beam-shaping technique. Two photons of the input mid-infrared (MIR) beam at 3000 nm are upconverted to 615 nm in the presence of a single photon at 1040 nm through the TSFG process. The focal-field engineering scheme studied here improves optical resolution and contrast of the TSFG imaging. We observe best improvement of ~43 % in the central-lobe full-width half diameter with ~35% side-lobe strength of that of the central-lobe with the use of optimum phase-mask using isolated amorphous silicon (a-Si) nano disks as the sample. We compare the contrast enhancement between the experiments and simulations as a function of varying grating pitch and find good overall agreement between the two. In addition to annular phase masks, we also demonstrate edge contrast enhancement by imaging gratings with higher-order Hermite-Gaussian beams profile generated using horizontally partitioned 0-𝜋 phase profile.

Age-related macular degeneration (AMD) is the leading cause of permanent vision loss and visual impairment in people over 60. Early detection of the disease is essential to prevent the evolution of the disease into an advanced stage. An eye care specialist has to perform a dilated eye exam, fundoscopy, a visual acuity test, and fundus photography to determine if a patient has macular degeneration and the stage of the disease. Most of the equipment used nowadays in eye care clinics is equipped in one system with both fundus camera and OCT technology that provides more comprehensive clinical evaluations. In most countries the healthcare system suffers from a low doctor to patient ratio; due to it, diagnosis can become time-consuming and error-prone. To minimize this downfall, a computer-aided diagnosis (CAD) strategy is proposed using machine learning techniques to predict the presence of age-related macular degeneration using both OCT and fundus images. The computer-aided diagnosis (CAD) is using a portable device, Jetson TX2 board, a powerful AI computer device, to predict the presence of an abnormality in the retina. A dataset composed of three categories: normal retina, dry AMD, and wet AMD from both OCT and fundus images have been used to evaluate the performance of different neuronal networks. Cost reduction and system portability are implemented with the proposed system for point of care in ophthalmology applications.

The development of the disease leads to changes in the biochemical composition of biological tissues. Therefore, determination of the composition is important for medical diagnostics. In recent years, Raman spectroscopy has been used to study biological tissues. However, Raman spectra of most tissue components overlap significantly, and it is difficult to separate individual components. The aim of our study is to investigate the possibilities of the multivariate curve resolution alternating least squares method for the analysis of in vivo Raman spectra. We used a portable conventional spectroscopy setup. The analysis of Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma and pigmented nevus was performed. As a result, we obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The classification of the Raman spectra of various diseases (malignant vs. benign neoplasms, malignant melanoma vs pigmented neoplasms) by the contribution of the spectra of the components shows the classification accuracy about 70%. The obtained results show the possibility of unmixing several spectrally similar components using the multivariate curve resolution alternating least squares analysis even under noisy conditions of the recorded Raman spectra. The method may be used for the analysis of Raman spectra with a low signal-to-noise ratio.

Raman spectroscopy has become widespread in non-destructive, label-free cancer diagnosis during a clinical examination. The development of efficient and flexible devices is allowing for increasing the information content of the recorded Raman spectra and the final diagnostic accuracy in real-time with a high signal-to-noise ratio. In the present work, we developed the design of a miniature fiber optic probe for the implementation of the Raman spectroscopy method for potential endoscopic applications. The optical model of a fiber optic probe was designed using software Zemax OpticStudio. We studied 1) the filtering unit for suppression of laser radiation in the collection channel of the Raman probe made at the entrance to the spectrometer; 2) a set of optical fibers with microstructures at the end as elements that form a probing area of the test sample; 3) optical coupling of a bundle of optical fibers with the entrance slit of the spectrometer. As a result, the influence and prevention of the effect of laser radiation interference between coaxially located filters were shown, which leads to a decrease in the total optical filtering density of the laser wavelength and, accordingly, to a decrease in the signal-to-noise ratio of the system.

Leaf water content is essential to measure the growth of plants and estimate the risk of drought or forest fire. Scientists have shifted their attention from conventional methods to spectroscopic techniques, as they can provide real-time water monitoring in plants from a remote accessed station. In the study, data were acquired from oven-dried leaves at various stages of heating. The correlogram between reflectance intensity and equivalent water thickness against wavelengths was used to identify the suitable wavelengths and associated reflectance ratios for further assessment of water content in the leaf. Based on the nature of acquired data, exponential and bi-exponential models were applied to relatively evaluate the optimal reflectance ratios for the determination of water content in leaves. Moreover, the results were compared with the water index (WI) reflectance ratio (R₉₀₀/R₉₇₀) and the three-band ratio index (RATIO₉₇₅). The WI reflectance ratio is typically used as a standard estimator of water content in plants while RATIO₉₇₅ is another suitable three-band ratio centered at 975 nm gaining wide acceptance. The observation in the study might be useful in finding the optimal indices for the qualitative assessment of leaf water content within the shorter range (600-1100 nm) of near-infrared spectroscopy.

Reflectance confocal microscopy (RCM) allows real-time in vivo visualization of the skin at cellular level. The study of RCM images provides information on the topological and geometrical properties of the epidermis. These may change in each layer of the epidermis, depending on the subject’s age and the presence of certain dermatological conditions. Studying RCM images requires manual identification of cells to derive these properties which is time-consuming and subject to human error, highlighting the need for an automated cell identification method. We propose an automated pipeline to analyze the structure of the skin in RCM images. The first step is to identify the region of interest (ROI) containing the epidermal cells. The second step is to identify individual cells in the segmented tissue area using an image filter. We then use prior biological knowledge to process the resulting detected cells, removing cells that are too small and reapplying the used filter locally on detected regions that are too big to be considered as a single cell. The results are evaluated both on simulated data and on manually annotated real RCM data. This study shows that automatic cell identification can be achieved, with an accuracy (precision and recall) that matches the inter-expert variability.