This PDF file contains the front matter associated with SPIE Proceedings Volume 12389, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The actual gap of the label-free quantitative phase microscopy in respect to fluorescence microscopy, that allows the subcellular characterization by using exogenous markers, is the lack of intracellular specificity. Recently, computational methods based on artificial intelligence have been demonstrated, which allow a virtual staining of single cells in both 2D and 3D, but they require co-registration systems able to collect simultaneously both fluorescence and quantitative phase information. However, a real limitation exists, i.e. these approaches cannot be used in flow cytometry condition. In this paper, we discuss a new methodology for adding the intracellular specificity analysis to tomographic phase microscopy in flow cytometry. The proposed strategy is based on the statistical clustering of tomograms voxels, thus allowing the segmentation of cell’s organelles. Here we report the results of nuclear region identification for cancer cells.

Morphological changes in neurons can denote cell health, growth, and death in response to environmental stressors. Quantitative phase imaging (QPI) has been used to assess neuronal network mass over time, which reveals such changes. High quality segmentation of cells in QPI is necessary to extract dry mass effectively. Neural networks are effective at segmentation but require vast amounts of data to train. Previously, we trained neural networks to segment neurons using simulated images that were generated from a biological neuron growth model. Images were simulated by approximating cell bodies as ellipsoids, and neurites as thin rectangular regions. The simplicity of the neuron images limited the quality of segmentation especially around neurites, which exhibit weak phase signals. In this work, improved segmentation quality is demonstrated by increasing the amount of complexity in the simulation. Namely, a data set of 5000 training images is procedurally generated by cropping cells from a sample of ten images. Cells are randomly placed, scaled and rotated into scenes of random noise and of background generated by our microscope. After training the network, its performance is tested on 100 images independent of the training data. This resulted in improvement in the Dice coefficient between the network output and the ground truth when compared with the best performing model.

Quantitative phase imaging (QPI) is an emerging label-free biomedical imaging modality. However, achieving high-fidelity QPI through an optical multicore fiber remains challenging. We demonstrate a novel phase reconstruction algorithm tailored for QPI through a lensless multi-core fiber (MCF) with a diameter of less than half a millimeter. Furthermore, precise reconstruction of the complex light field enables digital refocusing, providing flexible working distance for the lensless endoscope. Moreover, a total variation model is implemented to reduce the noise in the reconstructed images, enhancing the endoscopic image quality. This powerful ultra-thin QPI probe could open new perspectives for endoscopic imaging.

The imaging quality of in-line digital holography is challenged by the twin-image and aliasing effects because sensors only respond to intensity and pixels are of finite size. As a result, phase retrieval and pixel superresolution techniques serve as the two essential ingredients for high-fidelity holographic imaging. In this work, we combine the two within a unified algorithmic framework. Pixel super-resolution phase retrieval is recast as an optimization problem and is solved via gradient descent-based algorithms. Regularization techniques and Nesterov’s momentum are introduced to further speed up data acquisition and iterative reconstruction. The proposed algorithms are verified through a proof-of-concept lensless on-chip microscope. We demonstrate experimentally the capability of pixel super-resolution phase retrieval techniques in revealing the subpixel and quantitative phase information of complex biological samples.

Quantitative Phase Imaging (QPI) has become a mainstay imaging technique in the biomedical sciences to study cells and other biological processes. Traditional QPI techniques are transmission-based and, thus, limited to thin samples. Over the past few years, multiple 3D QPI tools have emerged attempting to overcome this limitation and provide cross-sectional phase information of thicker samples. However, most of these techniques remain transmission-based, which constrains their ability to image samples thicker than a few mean free scattering lengths. Recently, we have developed quantitative oblique back-illumination microscopy (qOBM) as an epimode technique that enables label-free quantitative phase imaging of thick samples with tomographic crosssectioning. Like in most 3D QPI instances, qOBM requires multiple captures to render a quantitative phase image. Specifically, qOBM requires four raw captures, obtained by illuminating the sample obliquely from four different directions, to reconstruct the quantitative phase. This muti-capture scheme hinders qOBM’s ability to investigate valuable fast dynamic processes, such as blood flow, as well as its usability in some in-vivo applications. Here, we present a deep-learning enabled single-capture version of qOBM that quadruples the system’s imaging speed and prevents motion artifacts. To this end, we have trained a U-Net GAN to learn the qOBM reconstruction from a single capture obtained with oblique illumination. We show the capabilities and limitations of this approach, as well as some of the novel applications that this system enables, such as in-vivo high-resolution non-invasive blood flow quantitative phase imaging.

Bortezomib is one of the most researched proteasome inhibitor drug in cancer cell research. Studying its effects, measuring and monitoring treatment response and effectiveness is a widely developing area in cancer research. The introduction of non-invasive measurement tools into the this research is a very important and desirable development, as it is a promising alternative to existing chemical tests. In our work we presented multimodal methodology connecting multiple non-invasive and label-free techniques to study effects of bortezomib on RPMI8226 cells. We connected digital holographic microscopy and holographic tomography with chemical specificity from Raman micro spectroscopy and we showed that treatment with bortezomib caused decrease of RI in the cells and their nucleolus and that changes in chemical compositions after treatment indicate cell apoptosis.

Metrology is a key aspect of any measurement technique since the information about the error is the enabling factor for their utility and developments. Quantitative phase imaging (QPI) benefit from metrology in particular, as new approaches to system design, data processing and relevant feature extraction methods require constant validation and improvements. QPI in biomedical applications span a wide range of measurement challenges ranging from sub-µm features in monolayer of cells to cm² cell cultures, free-floating and three-dimensional cell clusters or even whole organisms. In this work we show how to create phantoms that strive to mimic all kinds of said specimens and their interaction with light, ultimately providing tool for validation and benchmarking a variety of QPI systems. We exploit many degrees of freedom provided by the two-photon polymerization technique–most notably three-dimensional shape and size, refractive index modulation and adjustable scattering properties–in order to design and fabricate various phantoms that resemble real biological microobjects, recreate the challenges of the particular measurement scenario and finally provide invaluable data for metrological analysis.

Quantitative oblique back-illumination microscopy (qOBM) enables quantitative phase imaging (QPI) in thick samples using epi-illumination. While qOBM offers unprecedented access to refractive index (RI) information in arbitrarily thick scattering samples, QPI-based (or RI index based) imaging still suffers from low cell nuclear contrast, which important for disease detection, including cancer. In this work, we use the acetowhitening effect of acetic acid to enhance the nuclear phase contrast of thick fresh tissue samples. Imaging results from brain samples are presented. Acetic acid phase staining may have important implications for in-vivo QPI-based disease detection

This contribution presents a joint phase compensation and autofocusing method for telecentric off-axis Digital Holographic Microscopy (DHM). Current challenges of off-axis DHM systems applied to in-vivo imaging are the automatic reconstruction of phase images without phase distortions while the specimens under research move within the volume, generating out-of-focus holograms. Although different proposals to tackle these challenges individually have been reported for static samples, in this proposal, both issues are solved concurrently with no additional user intervention. As a result, in-focus compensated phase images of the out-of-focus studied samples are obtained. The proposal has been validated using simulated data.

Digital holographic microscopy (DHM), which provides quantitative phase imaging (QPI), has been widely applied in material and biological applications. The performance of DHM technologies relies heavily on computational reconstruction methods to provide accurate phase measurements. For example, non-telecentric DHM systems should compensate for the spherical wavefront associated with a non-telecentric configuration. The size of the ±1 diffraction orders in the hologram spectrum depends inversely on the radius of the curvature of the spherical wavefront introduced by the non-telecentric DHM system. Therefore, one can estimate the radius of curvature of the spherical wavefront by analyzing the hologram spectrum. Here, we outline the steps for the automatic reconstruction of phase images without distortions and with minimum user input from a hologram recorded in a non-telecentric DHM system. The proposed reconstruction approach can be divided into six main steps. The first step automatically selects the +1 diffraction order in the hologram spectrum. Secondly, the spherical wavefront parameters and the interference angle are estimated by analyzing the size and position of the selected +1 order. The third and fourth steps are the spatial filtering of the +1 order and the compensation of the interference angle, respectively. The next step involves the estimation of the center of the spherical wavefront. Finally, there is a fine-tuning step to optimize the estimated parameters and provide a phase image with minimum phase distortions. We have identified the relevant metrics in each step, compared multiple approaches, and selected the one with the higher performance for all our experimental holograms.

Existing quantitative phase imaging (QPI) techniques are faced with an inherent trade-off between phase imaging fidelity and temporal resolution. Here, we propose a general algorithmic framework for QPI reconstruction that enables frame-rate-limited holographic imaging. It takes an inverse problem approach by formulating phase retrieval as a nonsmooth nonconvex optimization problem. Efficient solvers for the problem are derived whose algorithmic behaviors have been studied from both theoretical and experimental perspectives. The proposed framework is applicable to various existing holographic imaging configurations, and makes it possible to incorporate advanced image priors for quality enhancement.

In a prospective observational pilot study, we evaluated label-free quantitative phase imaging (QPI) with digital holographic microscopy (DHM) as a tool to describe changes in biophysical properties of lymphocytes and monocytes after cardiac surgery. The results of our study show the capability of DHM to quantify perioperative lymphocyte and monocyte alternations in cardiac surgery patients. The patterns of biophysical DHM data correlated with laboratory parameters, flow cytometric cell markers, and postoperative course. This exemplifies DHM as a promising tool to characterize and assess inflammatory processes and course of disease.

Holographic molecular binding assays detect target molecules binding to the surfaces of specifically functionalized probe beads by measuring the associated increase in bead diameter with holographic video microscopy. Holograms of individual colloidal beads are analyzed by fitting to analytic predictions of the Lorenz-Mie theory of light scattering, yielding measurements of bead diameter with the nanometer precision required to detect binding events. Holographic binding assays share the specificity and robustness of industry-standard bead-based assays. Direct holographic readout eliminates the processing time, expense and uncertainty associated with fluorescent labeling. The underlying technology for holographic particle characterization also has a host of other applications in biopharmaceuticals, semiconductor processing and fundamental research.

Digital holographic microscopy (DHM) has been demonstrated to be a suitable label-free and non-invasive quantitative phase imaging (QPI) tool in risk assessment of the cytotoxic potential of engineered nanoparticles and organic nanocarriers. For a broader application, robustness of DHM-based assays needs to be demonstrated towards DHM standardization in risk assessment. Thus, we performed an interlaboratory comparison on the transferability and reproducibility of a DHM-based assay. The cytotoxic potential of organic nanoparticles on A549 lung epithelial cells was analyzed in two European laboratories using identically constructed DHM systems. Our results demonstrate a solid and accurate performance of the DHM-based cytotoxicity assay

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables threedimensional phase imaging of thick samples with epi-illumination. Here, we present a preliminary study using qOBM to monitor sickle cell disease in mice. We have used qOBM to image the brains of recently sacrificed PBS-perfused control mice and mice with sickle cell disease. Quantitative phase images revealed morphological differences in the blood vessel structure coupled with blockages of cortex vessels where potential strokes occurred. We demonstrate that qOBM enables visualizing these differences with future applications for in-vivo monitoring of sickle cell blood flow.

Laser-based wide-field coherent imaging methods suffer from low image contrast due to the speckle noise as well as poor lateral resolution using collimated illumination. To improve the image contrast and spatial resolution for label-free cell imaging applications, we propose a new dynamic speckle illumination scheme using perfect optical vortex (POV) beams that can provide finer speckles with more uniform distributions. The low spatial coherence from the POV speckle field has significantly improved the signal-to-noise ratio (SNR) and the image contrast, thus contributing to a high spatial resolution that matches the diffraction limit in our cell imaging experiments. Importantly, the depth-resolved imaging capability has been realized which has allowed us to visualize fine subcellular structures at different focal planes.

Zernike polynomials are orthogonal polynomials that form a complete basis set and can be easily used to describe aberrations present in an optical system. Zernike modes find applications in various fields like adaptive optics (AO), optical imaging, ophthalmology, free space optical (FSO) communication, etc. Since the modes are orthogonal, they can express any arbitrary wavefront as their linear combinations. The orthogonality of the modes enables the calculation of the expansion coefficients and suggests the independent behaviour of the Zernike mode. In this work, we numerically estimate the wavefront, defined as Zernike modes, using various state of the art phase retrieval methods. We use the Zonal wavefront sensor (ZWFS) and Transport of Intensity Equation (TIE) for phase reconstruction and then calculate the orthogonality between reconstructed Zernike modes. It is found that the reconstructed Zernike modes are not perfectly orthogonal, which is mainly due to the discrete representation of the Zernike modes. We further investigate how the change in the number of zones in a ZWFS affects orthogonality. We also simulate TIE to retrieve the phase and compare the orthogonality results with ZWFS. This study will be helpful in applications where a wavefront described using Zernike mode needs to be reconstructed, and improvement in the orthogonality is required, which is achieved by increasing the number of zones in the ZWFS and representing Zernike modes in a more continuous form.

The Hilbert transform (HT) method is a technique to perform optical phase retrieval from a single off-axis interference pattern. However, one of the significant issues with the HT method is the error generated due to noise in the recorded interference pattern, which can be avoided using a proper pre-filtering step. The importance of pre-filtering of the interference patterns is studied in this study. The observations are verified experimentally by performing quantitative phase imaging of a white blood cell (WBC) using a Mach-Zehnder interferometer configuration.

In quantitative phase imaging, spatial resolution and its influencing factors have not been fully explored. Here, we propose to define phase resolution based on the Sparrow limit and investigate the effect of phase inequivalence between adjacent object points. To simulate the measured object phase distribution, the analytical solution to the complex scattered field from a thin phase object is first obtained by solving the inhomogeneous wave equation in the wavevector space. Our theory shows that the phase resolution is not only related to the illumination wavelength and the numerical aperture of the imaging system, but also the object size and the phase detection signal-to-noise ratio. We have validated our findings by simulating phase images of different point arrays and two-point objects under different noise levels.

Quantitative phase microscopy (QPM) has recently become indispensable technology for label-free quantitative analysis of various biological cells and tissues, such as, sperm cells, liver sinusoidal cells, cancerous cells, red blood cells etc. The key parameters controlling measurement accuracy and capability of QPM system depends on its spatial and temporal phase sensitivity. The spatial phase sensitivity of QPM is governed by coherence properties of light source and temporal stability depends on optical interferometric configuration. Most of the QPM techniques utilize highly coherent light sources like lasers benefited by their high spatial and temporal coherence, and brightness. But high spatio-temporal coherence leads to occurrence of speckle noise and spurious fringes leading to inhomogeneous illumination and poor spatial phase sensitivity. We have developed QPM systems using partially spatially coherent monochromatic (PSCM) light sources which guarantees high contrast interferograms over large field-of-view to increase space-bandwidth product of QPM system by ten-times and demonstrated ten-fold improvement in spatial-phase sensitivity and phase measurement accuracy compared to coherent laser light. By means of using PSCM with common path configuration we could also achieve ten-fold temporal phase stability. We have demonstrated advantages of PSCM based QPM in various industrial and bio-imaging applications. Experimental results of reduced speckle noise, free-from spurious fringes, spatial phase sensitivity using industrial objects are demonstrated and compared with highly coherent light using single mode fiber. Finally, phase map of biological samples is also presented with high accuracy in phase measurement. Thus, the use of PSCM light in phase microscopy, holography of realistic objects, i.e., industrial and biological samples leads to high accuracy in the measurement of quantitative information.

In this contribution, we present a study of the spatial resolution and quantitative phase imaging (QPI) performance of digital lensless holographic microscopy (DLHM) when holographic optical elements (HOEs) are employed as the illumination source of the system. The HOE employed in this study is a transmission hologram of a 3µm pinhole illumination system. We have quantified the imaging performance of the studied DLHM implementation by using a tailormade phase USAF test target. Results were then used to validate the dependence of the spatial resolution and phase sensitivity on the numerical aperture of the holographic point source.

We present a non-invasive study of the zebrafish brain to understand the vasculature and changing cellular dynamics in the cerebral stroke model using swept-source optical coherence tomography/angiography (SSOCT/A). An ischemic stroke is caused by reduced or obstructed blood supply in the brain, eventually leading to cell death due to insufficient oxygen and nutrient levels. The aberrant/anomalous blood flow characteristics in ischemic stroke are analyzed by phase variance and the doppler method using SSOCT. The subsequent cell mortality is monitored using the speckle-contrast technique of SSOCT. The SSOCT technique for disease surveillance provides fast acquisition time, reduced motion artifacts, label-free visualization, and non-invasive examination.