This PDF file contains the front matter associated with SPIE Proceedings Volume 12629, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Imaging Mueller polarimetry has already proved its potential for biomedical applications. However, tissue characterization utilizing all 16 elements of the Mueller matrix (MM) is not straightforward and requires data postprocessing decomposition algorithms. We developed the theoretical framework and performed the experimental studies on extracting the polarimetric parameters of phantoms and biological tissue while using only part of MM elements and validating them against the results of Lu-Chipman decomposition of corresponding complete MMs. Our findings open an avenue for developing simple and compact polarimetric systems operating at video rates that can be translated to clinics for real-time tissue diagnosis and monitoring.

Long-term storage and cryopreservation of biological tissues is a global challenge in the fields of regenerative medicine, tissue engineering and implants, because of the formation of large ice crystals that may damage cellular membranes and extracellular matrix (ECM) of collagen. In our studies different thawing mechanisms were tested to achieve more uniform warming of 3D in vitro tissue models, prepared from magnetic nanoparticles (NPs)-modified 3D electrospun nanofiber fleeces and fibroblasts. 3D tissue models frozen in liquid nitrogen were defrosted either with water bath or with radio-frequency (RF) inductive heating of the magnetic NPs and their morphology was compared to that of a non-frozen tissue model using a transmission Mueller microscope and thin sections of all three types of tissue models. Our results demonstrate a sensitivity of the polarimetric parameters obtained with the differential decomposition of Mueller matrices to small changes in sample morphology caused by the different thawing methods. A detailed statistical analysis proved the statistical significance of the experimental data from the three groups of all tissue models.

Many pathologies leading to the death of the organism are tied to the cessation of cardiac or respiratory activity. In the present study, we studied the microscopic structure of the myocardium of male Wistar rats under conditions of acute cardiac and respiratory arrest. Unstained histological sections of the myocardium are examined with a polarizing microscope for the length of the sarcomere, A- and I-discs measurements. The results of the study showed that the length of the sarcomere during respiratory arrest and cardiac attack significantly decreases due to the length of the I-disc, especially in acute cardiac attack. Correlation-regression analysis showed sharp decrease of correlation in acute respiratory arrest, reaching extremely low values in cardiac attack. The study allows assessing ultrastructural changes in the myocardium and forms the basis for diagnosing acute heart pathology with the machine learning and automated analysis.

Optical dispersion would affect ultrashort laser pulse duration and thus decrease energy concentration. The instantaneous peak power will be reduced and affect the nonlinear optical absorption. To detect and compensate the optical dispersion dynamically, we have integrated the real-time ultrashort pulse measurement based on direct optical-dispersion estimation by spectrogram (DOES), the DOES could be used to find the group delay dispersion (GDD) from single shot spectrogram with fast and accurate computation time. the ultrashort pulse compensation system consist of a blazed grating and deformable mirror (DM). The GDD is used to drive the DM compensation by digital PI controller implemented on field programmable gate array (FPGA). The compression system could be operated at 100 Hz in real-time and is implemented to multiphoton excited fluorescence microscopy (MPEFM) system. The experimental result shows the static and dynamic dispersion could be compensated in 50 ms, and the overall nonlinear excited efficiency could increase to 1.4-fold, which is the theoretical limit based on the current setup.

Over the past several decades, research and innovations in the field of cancer therapy have garnered significant attention globally. Despite these advancements, incidences of recurrence and relapse have been a major obstacle in cancer treatment. Moreover, the underlined cause of death due to cancer is largely due to delayed detection and therapy resistance. It is evident that early detection and prediction of therapy resistance can benefit in designing personalized treatment regimen and better prognosis. Therefore, vibrational spectroscopic techniques such as Raman spectroscopy (RS) are being explored. RS is a rapid, inexpensive and objective method that extracts useful biochemical information. It provides a global molecular fingerprint of the cellular milieu, highlighting the physiological state of the system. In this study, we have evaluated RS to characterize and delineate chemoresistant and parental cell types of cervical and liver cancer cell lines HeLa, SiHa, HepG2 and Hep3B respectively. Doxorubicin-induced resistant cell lines were developed using incremental drug dosing method to obtain a desirable resistance index value. Raman spectra of these cell lines were recorded to understand biochemical profiles and stratify resistant and parental groups using confocal Raman microscope (WITec alpha 300R) and were preprocessed. Mean spectra were computed and were subjected to multivariate analyses such as Principal Component Analysis (PCA) and Principal Component based Linear Discriminant Analysis (PC-LDA). Key spectral features such as protein, DNA, lipid and cytochrome levels were observed higher in the resistant cell populations than that in corresponding parental cell populations. PC-LDA results showed good classification between parental and resistant cell populations for all the cell lines. Thus, RS can be prospectively explored in clinical settings for better stratification of cancer patients, enabling effective therapeutic outcomes.

The production of high yields of viable cells, especially Mesenchymal stem cells (MSCs), is a crucial yet challenging aspect in the field of cell therapy (CT). While progress has been made, there is still a need for quick, non-destructive ways to check the quality of the cells being produced to enhance cell manufacturing process. In light of this, our study aims to develop an accurate, interpretable machine learning technique that relies solely on bright-field (BF) images for enhanced differentiation of MSCs under different serum-containing conditions. Our investigation centers around the expansion of human MSCs derived from bone marrow cultivated in two specific media types: serum-containing (SC) and low-serum containing (LSC) media. The prevalent method of chemical staining for cell component identification is often time-intensive, costly, and potentially harmful to cells. To address these issues, we captured BF images at a 20X magnification with a Perkin Elmer Operatta screening system. Utilizing mean Shapley Adaptive exPlanations (SHAP) values obtained from the application of the 2-D discrete Fourier transform (DFT) module to BF images, we developed a supervised clustering approach within a tree-based machine learning model. The results of our experimental trials revealed the Random Forest model's efficacy in correctly classifying MSCs under varying conditions with a weighted accuracy of 80.15%. A further application of the DFT module to BF images significantly increased this accuracy to 93.26%. By transforming the original dataset into SHAP values using Random Forest classifiers, our supervised clustering approach effectively differentiates MSCs using label-free images. This innovative framework significantly contributes to the understanding of MSC health, enhances CT manufacturing processes, and holds potential to improve the efficacy of cell therapies.

We have developed methods which allow us to analyze images obtained with high resolution scanning laser ophthalmoscope (SLO). Registered retinal vessels can be extracted and quantified using image processing methods. Obtained data can be further analyzed for calculations of vessel morphological parameters.

The invention of the random laser has opened a new frontier in optics, providing also the opportunity to explore new possibilities in the field of sensing. Random lasing have been proposed as promising opportunity to extend the potentiality given by optical sensing strategies, in particular in the field of the measurement of diffusive properties. Compared to the other used strategies, random laser-base systems has the advantage to show amplification of the signal by stimulated emission, as well as spectral modification. In particular, a non-invasive type of random laser sensor, that exploits a transparent physical separation between the gain material and the diffusive sample, has been reported. Here we present an improvement of the experimental setup used for such a kind of sensor. By the use of a optical fibers system and a couple of twin sensors, we report an enhancement of the accuracy, stability, reproducibility, as well an measurement method easy to perform, without resorting to complicated numerical or analytic inversion procedures. Since the possibility to perform local direct measurement on diffusive samples, such a “active” method can be a promising strategy in the field of biomedical optics and for non-invasive diagnostic purposes.

Pulsed photothermal radiometry involves measurements of transient changes in blackbody emission from a sample surface after irradiation with a short light pulse. From such a radiometric record, light-induced temperature field inside the sample can be reconstructed by solving the inverse problem of heat diffusion and radiation. In principle, this enables threedimensional visualization of selectively absorbing structures inside strongly scattering biological tissues and organs, a.k.a. photothermal tomography (PTT). We present an up-to-date realization and testing of PTT in an agarose tissue phantom with a suspended human hair, imitating a subsurface blood vessel. After irradiating the phantom with a milisecond laser pulse at 532 nm, its surface was imaged with a fast mid-infrared (IR) camera equiped with a microscope objective. A custom code was used to reconstruct the laser-induced temperature field in three dimensions by running multidimensional optimization based on analytically formulated forward problem of heat transport and IR emission, using the projected -method algorithm. We demonstrate that quadratic binning of the radiometric record enables a 10-fold reduction of the computational time without adversely affecting the results. In the presented example, a sharp image of a hair at a subsurface depth of <200 μm with no significant noise or artifacts elsewhere in the imaged volume of 3 × 3 × 0.6 mm³ was obtained in only 45 seconds.

We investigate age-related changes of the dermal reduced scattering coefficient in human skin using a recently introduced methodology for non-invasive characterization of structure and composition of skin in vivo. The approach combines pulsed photothermal radiometry (PPTR) with diffuse reflectance spectroscopy (DRS) in visible part of the spectrum. The experimental data are fitted simultaneously with the respective predictions of a dedicated numerical model of light and heat transport in healthy skin (i.e., inverse Monte Carlo). For this purpose, we apply a four-layer optical skin model consisting of epidermis, upper dermis, lower dermis, and subcutaneous adipose tissue. The study is based on 24 measurements of test sites on the ventral side of the forearm in 9 women and 9 men with healthy fair skin, between 20 and 65 years old. Linear regression analysis of the assessed dermal reduced scattering coefficient values at 500 nm (a_der) indicated no significant variation with the person’s age. Meanwhile, strong correlations of a_der with the blood contents in both papillary and reticular dermis were observed. Separating the respective contributions of these three variables using multiple linear regression (MLR) analysis revealed a highly significant influence of person’s age on a_der (with Pearson’s correlation coefficient r = –0.55 and p < 0.0001). Specifically, by excluding the direct influence of the dermal blood contents, a_der decreases with age by approximately 0.2 mm^-1 per decade. In addition, the values obtained for older persons are in good agreement with the results from a large cohort study performed by Jonasson et al. (J. Biomed. Opt. 2018).

An accurate tumor delineation in neurosurgery is still a very challenging problem which we are addressing with optical coherence elastography (OCE). Because of the highly viscoelastic properties of brain tissue, we developed a new Air-Jet based tissue excitation source and evaluated the tissue stiffness with a 3.2 MHz swept-source Optical Coherence Tomography (OCT) system with a line scan rate of 2.45 kHz. The phase based displacement per pixel is measured and stiffness maps are calculated for brain tumor samples. However, certain features in the stiffness maps are seemingly not correlatable to the tissue features in the histological sections. Therefore, the structural properties of the histological sections e.g. fiber orientation, cell nuclei concentration and the “onion structure” with their rotational direction for meningioma were given greater consideration. The structural information are extracted from the histological sections via color deconvolution and structural tensor analysis. First results show that the stiffness transitions correlate with some structures of the histological sections. In summary, the Air-Jet OCE seems to be capable of measuring the stiffness as well as the structural composition of the sample. The long-term aim of this project is to establish OCE to support tumor delineation in the field of neurosurgery.

Brillouin microscopy has emerged as a non-invasive and label-free technique to map micro-mechanical properties of cells. Here we apply Brillouin microscopy to probe reorganization of F-actin network in respiratory cells treated with Timothy grass pollen protein extracts. The results of our measurements in conjunction with clustering data analysis confirm spatial cellular reorganization of F-actin proteins and compromised junctional integrity in treated cells as compared to controls.

This work is a contribution to the current effort in developing rapid, robust and cost-effective technologies for antimicrobial classification according to their mechanism of action, which could potentially serve as a new MoA detector. We propose an original, label-free technology based on time-lapse Digital Inline Holographic Microscopy, which will be coupled to deep-learning analysis in a later stage. This paper focuses on the physical analysis of the reconstructed time-lapse phase image at the single-cell level, and shows that time-lapse DIHM is able to capture different phenotypes linked to different antibiotic classes. PCA analysis based on 10 antibiotics for 4 inhibiting targets shows that we can discriminate 5 over 6 chemical classes in less than 2 hours.

We have developed a high resolution scanning laser ophthalmoscope optimized for imaging the morphology and dynamics of the retinal vessels. The system has flexible control over the imaging field of view allowing for easy navigation on the retina and selection of the desired vessel for high magnification imaging. We have also developed image processing methods that allow for extraction and quantification of vessel walls and lumen that serve for calculation of various morphological parameters.

The fabrication of artificial tissue and organ models is one of the important directions of the development of modern biomedicine. Assessment of the morphology, maturation, and viability is an important part of such developments. Here, we report on the validation of our custom-build fluorescence spectroscopy (FS) system with optical coherence tomography (OCT) for assessing the metabolism and morphology of the full-thickness skin equivalence (FSE) model. FS along with OCT has been used for the metabolic activity evaluation of the developed FSE model and 3D imaging of its structure. Thus, we have developed a multimodal optical system that can be used in the future for a full-profile assessment of the maturation and viability of 3D-printed models of biological tissues in time-course development.

Polarimetric data is nowadays used in the biomedical field to inspect organic tissues or for the early detection of some pathologies. In this work, we present a thorough comparison between different classification models based on several sets of polarimetric data, this allowing us to choose the polarimetric framework to construct tissue classification models. Four different well-known machine learning models are compared by analyzing three polarimetric datasets: (i) a selection of ten representative polarimetric observables; (ii) the Mueller matrix elements; and (iii) the combination of (i) and (ii) datasets. The study is conducted on the experimental Mueller matrices images measured on different organic tissues: muscle, tendon, myotendinous junction and bone; all of them measured from a collection of 165 ex-vivo chicken thighs. Provided results show the potential of polarimetric datasets for classification of biological tissues and paves the way for future applications in biomedicine and clinical trials.