This PDF file contains the front matter associated with SPIE Proceedings Volume 12391, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Pathogens such as bacteria and fungi express various autofluorescence markers such as NAD(P)H, flavins, porphyrins etc. In contrast to steady state fluorescence, wherein the amplitude variations are more sensitive to bacterial growth state, time dependant fluorescence characteristics of these autofluorescence biomarkers are more robust and can potentially provide higher specificity of detection especially in various clinical and industrial settings. However, fluorescence lifetime measurements are typically carried out using bulky and expensive instruments. Herein, we demonstrate an extremely portable device 10 cm*10 cm*5 cm for fluorescence lifetime measurement comprising of pulsed LED sources, optical filters and single photon avalanche photodetectors (SPADs). The entire instrument is driven by an embedded hardware board and the photon arrival times from the SPAD are measured using a Texas instrument TDC counter interfaced with the hardware. First, the instrument response function of the device is measured and is found to be <1 ns suitable for measuring the fluorescence lifetime of various pathogens. Subsequently, fluorescence lifetime measurements of pathogens such as Staphylococcus Aureus, Pseudomonas Aeruginosa and E.coli is done under 365 nm light excitation. Using the autofluorecence lifetime characteristics and classification algorithms, we demonstrate bioburden detection in water with sensitivity exceeding 90% with a minimum detection limit of 10³ CFU/mL. Furthermore, a handheld version of the device is also developed for assessment of the pathogens directly on the clinical samples such as ulcers, burns, surgical site infections. Notably, the handheld device is able to track bacterial cell growth, differentiate cancer tissues from noncancerous samples and is also able to detect microbial growth in flesh samples demonstrating its applicability in a range of potential applications.

High-resolution and label-free histological imaging modalities provide cell nuclear contrast that is analogous to standard hematoxylin and eosin (H&E) histological staining. Recently, a rapid and slide-free imaging technique, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP), has been developed to image thick and unprocessed tissues with subcellular resolution. Here, we propose a fast and low-cost light-emitting diode (LED) based CHAMP imaging system assisted by deep learning. The low-resolution widefield LED images can be translated into high-resolution LED-CHAMP images that highly resemble Laser-CHAMP images by enhanced super-resolution generative adversarial networks (ESRGAN). Moreover, LED-CHAMP images can be further translated into virtual H&E-stained images comparable to standard H&E histology by virtual staining models. The versatility of LEDCHAMP is experimentally demonstrated using mouse brain thin slices and thick sections, which takes only five minutes for imaging tissue surface area with 10 × 10 mm². The promising LED-CHAMP workflow enables fast, low-cost, and comparable image quality for intraoperative assessment.

Cancer cells switch to glycolytic metabolic states even in aerobic environments to support enhanced growth and cellular functions. This phenomenon is known as the Warburg effect, and it inspires advancing interests in targeting metabolism for cancer therapy. Optical metabolic imaging (OMI) measures the fluorescence intensity and lifetime of the co-enzymes reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD). OMI can quantitatively distinguish cellular metabolic activities in a label-free manner. The goal of this study is to identify key metabolic pathways of cancer cells using NADH and FAD fluorescence lifetime imaging and machine learning methods. MCF-7 breast cancer cells were exposed to different culture media and inhibitors to disturb their metabolic activities, and NADH and FAD fluorescence lifetime imaging were performed by a multi-photon microscope. Here, we proposed a potential method of training convolutional neural networks to predict cellular metabolic states. Adapting convolutional neural networks for the prediction of cancer cell metabolic conditions was anticipated to provide substantially better performance than traditional models with extracted features. In summary, this investigation offers a non-invasive, quantitative technology to detect metabolic perturbations at a cellular level, which improves the identification of different metabolic states of cancer cells

All organisms carry out metabolic processes to produce chemical energy, but these biochemical pathways are not perfectly efficient and energetic waste is lost as heat. The interplay of heat production/retention/loss in endotherms has been well studied via thermal imaging. However, there is a striking absence of literature on the thermal output of ectotherms, especially invertebrate animals. We have developed a new thermal imaging technique to investigate waste heat production in the nematode worm C. elegans. No direct measurement of this metabolic waste heat has been made in C. elegans or any other mesoinvertebrate using IR imaging techniques.

In this study, thermal IR imaging was used to examine the difference in heat output between living and dead C. elegans. Living and dead C. elegans were imaged simultaneously providing a way to directly compare the temperatures of the worms. Temperature difference was used as a marker of difference in waste heat production, not absolute temperature. A cold object was used in reflectance mode to suppress the thermal background of the imaging substrate. Several different substrates with differing thermal properties were tested to minimize thermal background. The tendency for C. elegans to desiccate necessitated the development of sample preparation techniques that ensured the survival of the animals during imaging. Imaging revealed that there is a clear, repeatable difference in the thermal output of living C. elegans compared to dead animals (whose metaobolic processes have ceased). This is exciting as it points to IR imaging as being a novel investigative tool to be applied to the study of metabolism in C. elegans. In the future this opens the door to screening genetic mutants with known metabolic defects, thus providing useful data for the study of genes that impact metabolism.

Whole slide imaging (WSI) is a common step used in histopathology to quickly digitize stained histological slides. Digital whole-slide images not only improve the efficiency of labeling but also open the door for computer-aided diagnosis, specifically machine learning-based methods. Hyperspectral imaging (HSI) is an imaging modality that captures data in various wavelengths, some beyond the range of visible lights. In this study, we developed and implemented an automated microscopy system that can acquire hyperspectral whole slide images (HWSI). The system is robust since it consists of parts that can be swapped and bought from different manufacturers. We used the automated system and built a database of 49 HWSI of thyroid cancer. The automatic whole-slide hyperspectral imaging microscope can have many potential applications in biological and medical areas.

The potential to differentiate between diseased and healthy tissue has been demonstrated through the extraction of morphological and functional metrics from label-free, two-photon images. Acquiring such images as fast as possible without compromising their diagnostic and functional content is critical for clinical translation of two-photon imaging. Computational restoration methods have demonstrated impressive recovery of image quality and important biological information. However, access to large clinical datasets has hampered advancement of denoising algorithms. Here, we seek to demonstrate the application of denoising algorithms on depth-resolved two-photon excited fluorescence (TPEF) images with specific focus on recovery of functional metabolic metrics. Datasets were generated through the collection of images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins from freshly excised rat cheek epithelium. Image datasets were patched across depth, generating 1012, 256-by-256 patches. A well-known U-net architecture was trained on 6628 low-signal-to-noise-ratio (SNR) patches from a previously collected large dataset and later retrained on a smaller 620 low-SNR patches dataset before being validated and evaluated on 88 and 304 low-SNR patches, respectively, using a structural similarity index measure (SSIM) loss function. We demonstrate models trained on larger datasets of human cervical tissue could be used to successfully restore metabolic metrics with an improvement in image quality when applied to rat cheek epithelium images. These results motivate further exploration of weight transfer for denoising of small clinical two-photon microscopy datasets.

Biochemical molecules (nucleic acids, proteins) have their spectral signature in mid-infrared (MIR) due to the vibrational modes of their molecular bonds. Hence, MIR spectroscopy is interesting for identifying molecules in biological tissues. However, water molecules have a very strong absorption in this spectral range and therefore constitute a major interferent for absorption measurements of biochemical species in an aqueous medium. Since a decade, the advent of Quantum Cascade Laser (QCLs), high brightness monochromatic sources, combined with the development of uncooled bolometer matrices, have paved the way for new research in the field of tissue micro-spectroscopy. Recently, MIR absorption imaging turned out to be promising for diagnosis, in particular thanks to QCLs, but still difficult in aqueous media. The aim of our work is to study the interest of phase shift measurement of aqueous samples by dispersion spectroscopy, around known absorption peaks of the species of interest. An imaging setup based on a Mach Zehnder interferometer was implemented, using QCLs sources and a microbolometer matrix, that allows both absorption and dispersion measurements in the 900 to 1660 cm⁻¹ spectral range. It was applied to SU8 photoresist patterns of different thicknesses in a 50 µm-thick cell filled with water. Absorption and phase shift spectra are extracted from images and respectively compared to Fourier transform infrared spectrometer (FTIR) measurements and Kramers Kronig transformation of them. These results with hydrated samples are encouraging and open interesting prospects towards label-free tissue diagnosis.

Slide-free microscopy techniques have been proposed for accelerating standard histopathology and intraoperative guidance. One such technology is quantitative oblique back-illumination microscopy (qOBM), which enables real-time, label-free quantitative phase imaging of thick, unsectioned in-vivo and ex-vivo tissues. However, the grayscale phase contrast provided by qOBM differs from the colored histology images familiar to pathologists and clinicians, limiting its current potential for adoption. Here we demonstrate the application of unsupervised deep learning using a Cycleconsistent Generative Adversarial Network (CycleGAN) model to transform qOBM images into virtual hematoxylin and eosin (H&E)-stained images. The models were trained on a dataset of qOBM and H&E images of similar regions in excised brain tissue from a 9 L gliosarcoma rat tumor model. We observed successful qOBM-to-H&E conversion of both uninvolved and tumor-containing specimens, as demonstrated by a classifier test. We describe several crucial preprocessing steps that improve the quality of conversion, such as intensity inversion, pixel harmonization, and color normalization. This unsupervised deep learning framework does exhibit occasional subpar performance; for example, as with GANs in general, it can create so-called “hallucinations”, displaying features not actually present in the original qOBM images. We anticipate that this behavior can be minimized with more extensive training and deployment of advanced ML techniques, and that virtual-H&E-converted qOBM imaging will prove safe and appropriate for rapid tissue imaging applications.

Digital holographic microscopy (DHM) combined with microfluidics provides high-throughput label-free imaging flow cytometry for generating physical data from heterogeneous samples like particle or cell mixtures. For reliable analysis of the sample fluid, e.g., for cell counting, it is crucial that all particles in the sample fluid flow are monitored within the field of view of the optical microscope camera. In this work we combine a DHM system with a microfluidic system that allows lateral hydrodynamic focusing within a rectangular microfluidic channel. We investigate the hydrodynamic focusing effect at different settings of the microfluidic pump system by analyzing the retrieved DHM quantitative phase images of living cells flowing through the hydrodynamically-focused sample fluid flow. Our results show that DHM represents a feasible tool for the label-free characterization of sample fluid flows in microfluidic systems with hydrodynamic-focusing capabilities.

Detection and quantification of intracellular structures is fundamental in biomedical sciences. New emerging inspection tools based on holographic microscopy and quantitative phase imaging can give answers to such critical demands. Holographic tomography (HT) systems are the best candidates for this challenge. Recently, HT has been demonstrated working in flow-cytometry (FC) modality. Results show that the novel HTFC tool is capable to furnish 3D visualization and quantifications of the different intracellular particles. In particular, here we report that exogenous nanographene oxide particles as well as endogenous lipid droplets can be detected, measured, and visualized in each flowing cell by label-free HTFC. This method opens the way for accurate and high-throughput measurements at the 3D single-cell level for different applications such as diagnosis of diseases, development of drug delivery applications, and examination of cell functionalities. Experiments and processing methods will be described, and several examples will be discussed.

Quantitative phase imaging (QPI) enables label-free optical-path-length measurement of biological samples with nanometer-scale sensitivity, which offers unparalleled access to important histological and biophysical properties of cells and tissues. However, traditional QPI methods require a transmission-based optical geometry and are thus restricted to thin samples, which prevents the use of QPI for in-vivo applications. In this work, we present the design, characterization, and experimental validation of a handheld rigid probe for QPI with epi-illumination, using an optimized lighting configuration to achieve high phase-contrast sensitivity. The approach is based on a recently developed technology called quantitative oblique back illumination microscopy (qOBM). We demonstrate the real-time operation of our system with the future goal of applying it to help guide human brain tumor margin assessment intraoperatively in vivo, among many other potential applications.

Currently, biological samples that are analyzed microscopically such as blood smears or tissue sections need to be stained to visualize important information such cell nuclei. This represents a major hurdle on the path to further automation, increases costs and can lead to significant lab-to-lab variation in terms of diagnostic results. Here, the authors will present a novel microscopy approach, based on UV Fourier Ptychography Microscopy, that is able to directly image blood smears and digital pathology samples without the need for staining.

Gastrinomas are gastrin-producing neuroendocrine tumors (NETs) located in the gastroenteropancreatic system. Gastrinomas are often small, multifocal, and found at late stages. Their unpredictable behavior and metastatic potential make it extremely challenging to develop therapeutic strategies. Surgery is the only potentially curative treatment for gastrinoma, but current tumor localization techniques such as intraoperative ultrasound and manual palpitation have poor sensitivity for small tumors, resulting in higher rates of recurrence and metastasis. Therefore, there is a strong clinical need for developing advanced intraoperative imaging technologies for tumor localization in treating gastrinoma. Polarized light imaging (PLI) is a promising method for label-free tissue characterization due to its sensitivity to micro and nanoscale structures, which are often influenced with the onset of cancer, but no works have yet investigated the application of PLI for gastrinoma localization.

To assess the suitability of PLI for gastrinoma localization, we imaged 11 formalin-fixed paraffin embedded (FFPE) specimens of gastrinoma using a five-wavelength Mueller Matrix Polarization Microscope. The Lu-Chipman decomposition was applied to spatial maps of the sixteen Mueller matrix parameters. Values for depolarization, diattenuation, and retardance were compared for regions of interest corresponding to tumor and adjacent tissues. There was significant difference between the average depolarization of the Brunner’s gland and tumors when imaged with light at 442, 543, and 632 nm (p<0.05), and the average diattenuation values of the two at 405 nm (p<0.05), suggesting that these properties could be used for label-free localization. These results motivate further study of the use of PLI for NET localization. Future steps include broadening the sample pool to other NETs and validating results in fresh tissue studies.

Polarized light interactions with biological tissues can reveal information regarding tissue structure, while spectral characteristics are closely related to tissue composition. An integration of both modalities in a compact system could better assist tissue assessment. This study aims to develop a polarized hyperspectral imaging (PHSI) system that fulfills both linearly and circularly polarized hyperspectral imaging for in vivo and ex vivo applications. The system is comprised of a white LED, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a hyperspectral snapshot camera. The system was calibrated to compute the full Stokes polarimetry. For tissue differentiation, fresh ex vivo mouse tissue specimens from kidney, liver, spleen, muscle, lung, and salivary gland of mice were imaged. The spectra of three features, named degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), were generated. A k-nearest neighbor (k-NN) classifier was trained with multi-class spectra and five-fold cross validation. It was found that DOP better differentiates tissue with an average accuracy of 0.87. Additionally, support vector machine (SVM) classifiers were trained to differentiate between each two of the organs, and it was determined that DOLP better identified kidney, liver, and spleen, whereas DOCP better identified muscle and lung tissues. Then, the setup was employed to image in vivo human fingers with and without a blood occlusion to qualitatively estimate oxygen saturation. Preliminary results demonstrate that both DOLP and DOCP reveal a distinction of oxygen saturation states. These results demonstrate the feasibility of the PHSI system for distinguishing between optical properties of tissues, which has the potential to reveal disease-related information for diverse medical applications.

Label-free quantification of polymerization is of significant importance in today's research as recent advancements in the polymer field have sparked numerous applications in the biomedical industry. Poly-((Ethylene Glycol) Methyl Ether)- Methacrylate (POEGMA) has been recognized as a replacement for PNIPAM for biomedical applications due to various factors such as biocompatibility and thermal response rate. With a rapid thermal response, POEGMA allows for a variety of biomedical applications including a potential drug delivery mechanism. In this study, a Photonic Crystal-Total Internal Reflection (PC-TIR) biosensor has been utilized to monitor POEGMA-144, which has been synthesized via an Atom Transfer Radical Polymerization (ATRP) grafting-from approach. Monitoring the optical resonance of this biosensor, it allows for quantification of the growth of a layer of an initiator on the sensor surface as well as the growth of the POEGMA on top of it. In addition, the PC-TIR sensor further allows real-time analysis of the polymer’s thermal responsiveness in an aqueous environment. At a well-defined, molecular weight specific, Lower Critical Solution Temperature (LCST) of 42°C, the POEGMA-144 polymer conformationally changes, becoming hydrophobic and collapsing after expelling any water between its PEG side chains. Using the PC-TIR biosensor, a definitive polymer collapse was observed at the LCST; similarly, a re-swelling of the polymer was observed as the ambient temperature of the polymer was allowed to cool back to room temperature.

Vertical nanostructures have been studied extensively and been reported to induce unique responses in cells, including the formation of clathrin coating and accumulation of actin filament which leads to cell membrane curvature around the nanostructures. In our previous works, we have demonstrated that arrays of plasmonic nanoantenna can be used as effective label free biosensors to study live cells when combined with IR spectroscopy in the reflection mode, known as metasurface enhanced IR spectroscopy (MEIRS). By fabricating these plasmonic metasurfaces on top of silica nanopillars, we demonstrate that we can expand the potential of MEIRS: increased protein IR absorption in the amide band and increased sensitivity of our biosensor to processes involving cellular interaction with vertical nanostructures.

UV-excited autofluorescence spectroscopy can provide information on the metabolic status of cellular systems, but applications to turbid media such as tissues can be complicated by the presence of multiple scattering, intrinsic absorption, and background fluorescence. Our broader aim is the sensing of cellular-level metabolic status in tissue based the real time assessment of autofluorescence signals using spectral phasor analysis. Previously, we analyzed metabolic responses in yeast cells embedded in turbid media containing significant background fluorescence from collagen. Not only were changes in metabolism detectable under these conditions, but responses associated with NADH- and NADPH-linked metabolisms could also be distinguished. NADH and NADPH are metabolic co-factors having nearly identical excited-state emission but playing significant and distinct roles in cellular metabolism. Here, we extend the phasor analysis approach by sensing metabolic responses of yeast cells embedded in turbid media containing hemoglobin as a source of optical absorption. A metabolic response is induced by chemical perturbation, e.g., by adding cyanide to inhibit cellular respiration or by adding peroxide to induce oxidative stress. We demonstrate that phasor analysis is a versatile tool, e.g., by showing that spectral response associated with changes to cellular metabolism versus optical absorption are spectrally distinct and cannot be accounted for using a two-component spectral model.

This work uses a picosecond pulsed laser in frequency-domain photoacoustic microscopy (FDPAM) for thermal imaging of the sample. It uses the principle of the frequency-domain photoacoustic (PA) spectroscopy employed for trace gas detection relying on the thermal effect caused by the absorbance of the sequence of picosecond pulses. The thermal effect is modulated to a periodic thermal wave using a mechanical optical chopper. The PA signal caused by the thermal wave is measured by a PA sensor containing a microphone connected to a lock-in amplifier. The FDPAM uses a high repetitive (100 kHz, 500 ps) picosecond pulsed laser, a kHz frequency microphone, a chopper, and a lock-in amplifier. The system is tested using a USAF resolution chart. The system imaged a chrome metal strip of thickness 120 nm and width 3.1 µm of a USAF chart. The system exhibits a PA signal with a maximum amplitude of 2x10^-4 V for a modulation frequency of 760 Hz. The signal is about four times lower than those obtained (8 x10^-4) by a 532 nm CW laser of power 3 mW. The SNR of the system for the USAF chart is 5.5 dB. The system has a lateral resolution of 3.1 µm. The imaging resolution of the system can be improved by using an objective with a lower f-number.

Plant cell walls are highly complex structures, which are adaptable to various environmental stresses. While many biochemical pathways have been discovered, cellular stress indicators are widely unknown. Here we demonstrate a label-free optical setup, that can map both mechanical properties and chemical composition in biological samples. We measured changes in mechanical stiffness and chemical response of rose leaf cells due to inoculation of the fungal disease Diplocarpon rosae. With this we are able to image fungal growth in a dynamical and high-resolution manner.

Raman hyperspectral imaging enables visualization and measurement of the distribution of iron-binding proteins, as well as assessment of the role of proteins involved in the regulation of intracellular iron transport and membrane trafficking in intact cells. To advance the mechanistic understanding of intracellular iron transport, we have performed Raman imaging in MDA-MB-231 wild-type breast cancer cells and compared them to CRISPR/Cas9-based knock-out cells of two intracellular iron homeostasis regulators: the Divalent Metal Transporter 1 (DMT1) and the small GTPase Rab4A. Multivariate singular value decomposition (SVD) analysis showed that the wild type vs. knockout cell populations of both genes could be separated into two distinct groups. Both DMT1 and Rab4A silencing have significant and distinct impacts in a variety of Raman spectra peaks, indicating a strong impact on cell metabolism. Label-free and non-invasive Raman imaging of DMT1 or Rab4A wild-type vs. knockout breast cancer cells should provide important insights into the regulation of intracellular iron homeostasis and cell metabolism in cells and tissues.

We present a stimulated Raman scattering (SRS) microscope integrated with a novel fiber-based light source. Our light source provides two synchronized pulse trains with 100 mW average power each, independently tunable in the range of 913 to 930 nm and 1024 to 1034 nm, respectively, thus enabling SRS measurements across the 990 to 1300 cm^-1 spectral range with a spectral resolution of 15 cm^-1. We demonstrate the SRS imaging of leukemic cells recorded in a few seconds. Our system may find potential application in biomedicine, in particular, helping to accelerate the diagnostics and follow-up treatment of leukemia patients.

Stimulated Raman scattering (SRS) has attracted significant attention recently for providing high-sensitivity and background-free chemical characterization without exogenous labeling. For the laser system of SRS, the current benchmark is the combination of a pico- or femtosecond mode-locked solid-state oscillator and a synchronously pumped optical parametric oscillator (OPO), offering two central wavelengths λ_pump and λ_Stokes to match the desired Raman modes. Despite great success, the phase-sensitive nature of degenerate OPO hampers its access to low-frequency Raman shifts, and the inability to independently adjust the second wavelength prevents the OPO source from electronic pre- resonance (EPR) detection for desirable molecules.
In this work, we demonstrated an SRS spectro-microscopy system driven by a multiple-plate continuum (MPC) supercontinuum laser source, whose spectrum spans from 600 nm to 1300 nm, offering capabilities of dual-wavelength tunability across the entire Raman active region (0 to 4000 cm^-1). We demonstrated SRS microspectroscopy across the fingerprint, silent, and C-H stretch Raman regions in acetonitrile solution. This novel light source allows significant contrast enhancement through EPR-SRS by tuning the pump wavelengths toward the absorption peak of dye molecules, exemplified by the C=C mode of Alexa 635. Moreover, single-wavelength SRS imaging of the Drosophila brain was presented. We envision that utilizing an MPC light source will substantially enhance the sensitivity and specificity of SRS by implementing EPR mode and spectral multiplexing.

The cotyledons of seven radish sprouts were measured in vivo by dynamic optical coherence tomography (OCT) for 12 days. The dynamic OCT analyzed the temporal fluctuation of the OCT signal using logarithmic intensity variance (LIV) to visualize biological and intracellular activities. The vessel structures appear in the intensity images, but they are barely visible in the early stages of development. However, the vessel structures exhibit high LIV than the cotyledon from the early stages of development, resulting in high visibility of the vessel structures in the LIV images. The length of a midrib segment was measured from the LIV images of a cotyledon. The measured length was compared to the mean LIV values calculated from the manually selected regions of interest in the midrib and the cotyledon. The length and mean LIV values of the midrib suggested the association of high LIV values with fast growth. In conclusion, dynamic OCT has the potential to study the growth of vessel structures from the early stages of development.

Optical coherence tomography (OCT) is low temporal coherence gated cross-sectional imaging modality which is noncontact, non-invasive and in vivo technology. In conventional OCT mainly a broad-band light source is used to achieve high axial resolution which is inversely proportional to spectral bandwidth of the light source. The most preferred light source in OCT is super-luminescent diode (SLD) or a supercontinuum light source, but they are bulky and costly. In addition, use of broad band light in OCT systems require dispersion compensation system because most of biological tissues are birefringence, therefore, it further adds complexity to the system. Further, use of highly spatially coherent light leads to speckle noise which degrades the image quality.

We report longitudinal spatial coherence (LSC) gated line-field optical coherence tomography (LF-OCT) of multilayer structures using partially spatially coherent monochromatic light with speckle-free and reduced crosstalk. The LSC properties of low-cost diode laser source was engineered to significantly reduce LSC length to achieve high axial-resolution. The OCT system was operated both in full-field and line-field mode and it was found that line-field version has better contrast and less crosstalk. Experimental results of LSC gated OCT of multi-layer samples both biological and industrial objects along with speckle contrast, interferometric fringe contrast and LSC length measurement will be presented. The use of a low-cost diode laser in the wavelength range 800 nm for high resolution OCT is important for many biological applications. In addition, the system does not require a dispersion compensation mechanism.

Doppler optical coherence tomography (DOCT) is a non-destructive imaging technique designed to measure the movement of a sample by applying the Doppler effect to optical coherence tomography (OCT) signal data. It was designed to acquire a tomography image of the tympanic membrane (TM) and a calculated Doppler signal in real time with OCT using the CUDA parallel processing algorithm while inducing vibration of the TM with an audio signal. Afterwards, the thickness of the TM inside the ROI was measured using software, and the degree of response was analyzed according to the thickness. To measure the tomographic thickness of the TM responding to sound waves, image processing was used to acquire the upper and lower boundaries of the TM. To reduce the error in thickness measurement according to the angle of the TM, the shortest distance between the upper and lower boundaries at each pixel was used to reduce the error in the thickness measurement. In addition, by mapping the thickness information to a two-dimensional array, the movement of the TM in response to sound waves was finally analyzed through a histogram according to the thickness of the TM. Finally, we were able to obtain the tendency of the response according to the thickness of the TM, and quantitatively analyze the change in the reactivity according to the area of the TM.

A deep-learning powered mechanism for detection of complex biochemical compounds with the WGM sensor which comprises various amount of sensing channels and different selectivity with hundreds of individual microcavities in each has been proposed. We demonstrate the possibility for accurate prediction of the biomarker’s concentrations in the complex aqueous solutions by training on non-linear highly specific time series signals of the intensities radiated by the microcavities. The impact of the deep-learning engine architecture and type of the sensor sample on the sensing performance has been studied.

Residual adhesive on tooth surface after bracket removal has to be identified at an advanced stage to avoid further effects on orthodontic and dental procedures, which has to be identified at an advanced stage. Since conventional visual inspection has a major limitation in identifying residuals, non-invasive identification with multi-dimensional assessments using swept-source optical coherence tomography (SS-OCT) is proposed for the precise identification of residual adhesive on the tooth surface during dental bracket replacements. The feasibility was examined using ex-vivo bovine teeth specimens after the removal of orthodontic implant from the tooth surface. Multi-dimensional assessments, such as residual adhesive boundary, color-scaled enface, adhesive area and thickness information were obtained using OCT to confirm the feasibility of the method. The detection algorithm finds the boundary which is between the dental surface and residual adhesive. The residual adhesive is separated based on the boundary. The area of residual adhesive is measured by an optical microscope and the detection algorithm. The difference between the optical microscope and detection algorithm measured area is lower than 10%. The results revealed that the performed OCT assessments can be beneficial for real-time application during orthodontic procedures as a primary inspection tool. Multi-dimensional assessment method used OCT and confirmed feasibility study shows that OCT can be used the detailed novel diagnosis system and effective tools for dental.

Without any staining or labeling process, label-free cell imaging has less damage to cells and is suitable for longitudinal observation of cell behaviors. The lensless digital holographic microscopy, which directly uses photoelectric sensor chip to collect holographic images, has the advantages of low cost, non-invasive, large field of view and high resolution, and has been widely used in label-free cell imaging. However, when the classical angular spectrum method is used to recover the complex optical field in lensless digital holography, the twin image shows up and has a serious impact on image quality. To improve the quality of the reconstruction, a multi-wavelength lensless digital holography method is used for cell imaging in this paper. During numerical reconstruction, autofocus is performed firstly, and then the object light field is restored by the multi-wavelength phase retrieval (MWPR) iteration method combined with the compressive sensing (CS) strategy. The experiment results show that the reconstruction method based on multi-wavelength illumination combined with compressive sensing can effectively eliminate the influence of twin image and improve the reconstruction quality. Compared with the reconstruction results of single wavelength hologram, our method can not only obtain highresolution labeled cell images with large field of view, but also obtain high-quality non-labeled cell video, which can be used as an effective technique for cell imaging and dynamic continuous observation in the future.

Neurodegenerative diseases are currently among the most common causes for low quality of life among US population. In addition to that, disabilities caused by neurodegenerative diseases, negatively impact economy by causing early workforce retirement. Existing human models of such conditions are hard to replicate and describe due to the wide array of factors influencing the progression and morphology of the disease. Recent studies suggest application of photodynamically induced oxidative stress in drosophila model as a viable trigger to neurodegenerative conditions. We hypothesize that neural degradation is accompanied by significant changes in brain chemistry and biomechanics. This preliminary study focuses on investigation of correlation between drosophila brain elasticity and induced oxidative stress using combined Brillouin-Raman spectroscopy as a label-free method of elasticity measurement.