This PDF file contains the front matter associated with SPIE Proceedings Volume 11249, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Multimode fibers are widely explored for optical communication, imaging and sensing applications. We develop a single-shot full-field temporal measurement technique based on a multimode fiber. The complex spatiotemporal speckle field is created by a reference pulse propagating through the fiber, and it interferes with a signal pulse. From the time-integrated interference pattern, both the amplitude and the phase of the signal are retrieved in spectral and temporal domains. The simplicity and high sensitivity of our scheme illustrate the potential of multimode fibers as versatile and multi-functional sensors.

We propose a self-reference interferometric method for phase calibration of spatial light modulator (SLM) based on two blazed gratings. Compared with traditional methods, the proposed method yields more stable results by generating fringes with lower spatial frequency, thus making it possible for accurate, low cost measurement of the phase modulation of an SLM.

We report a generative adversarial network (GAN)-based framework to super-resolve both pixel-limited and diffraction-limited images, acquired by coherent microscopy. We experimentally demonstrate a resolution enhancement factor of 2-6× for a pixel-limited imaging system and 2.5× for a diffraction-limited imaging system using lung tissue sections and Papanicolaou (Pap) smear slides. The efficacy of the technique is proven both quantitatively and qualitatively by a direct visual comparison between the network’s output images and the corresponding high-resolution images. Using this data driven technique, the resolution of coherent microscopy can be improved to substantially increase the imaging throughput.

Optical diffraction tomography (ODT) is a powerful label-free three-dimensional (3D) quantitative imaging technique. However, current ODT modalities require around 50 different illumination angles to reconstruct the 3D refraction index (RI) map, which limits its imaging speed and prohibit it from further applications. Here we propose a deep-learning approach to reduce the number of illumination angles and improve the imaging speed of ODT. With 3D Unet architecture and large training data of different species of cells, we can decrease the number of illumination angles from 49 to 5 with similar reconstruction performance, which empowers ODT the capability to reveal high-speed biological dynamics.

We present two computational illumination strategies for high-speed Intensity Diffraction Tomography (IDT) on dynamic, unlabeled biological samples. IDT provides quantitative volumetric reconstructions of biological samples but is slow, requiring hundreds of images under diverse illumination. We developed multiplexed IDT (mIDT) and annular IDT (aIDT) as software-based and hardware-based solutions improving IDT’s acquisition speed. mIDT optimally combines illuminations into each image for faster acquisition while aIDT uses fewer, single illuminations from a ring-geometry LED grid to achieve hardware-limited 5Hz and 10Hz volume rates, respectively. We demonstrate the improvement these techniques provide on living epithelial buccal cells and Caenorhabditis elegans worms.

It has recently been recognized that compressed sensing, especially dictionaries and related methods, formally map to machine learning architectures, e.g. recurrent neural networks. This has led to rapid growth in algorithms and methods based on deep neural networks (but not only) for solving a variety of inverse and computational imaging problems. In this paper, we review these developments in the specific context of quantitative phase imaging and emphasizing the impact of object power spectral density and noise properties on the quality of the reconstructions.

Fluorescence microscopy has been proven a valid method of classifying sperm with different characteristics such as gender. However, it has been observed that they introduced an increase in oxidative stress as well as undesired bias. We show that spatial light interference microscopy, a QPI method that can reveal the intrinsic contrast of cell structures, is ideal for the study of sperm. To enable high-throughput sperm quality assessment using QPI, we propose a new analysis method based on deep learning and the U-Net architecture. We show that our model can achieve satisfying precision and accuracy and that it can be integrated within our image acquisition software for near real-time analysis.

Typical quantitative refractive index (RI) imaging methods (e.g. optical diffraction tomography) use coherent illumination and interferometric detection to reconstruct 3D phase objects. We have developed a new technique to perform accurate ODT reconstructions using spatially incoherent illumination. This technique reconstructs the RI by minimizing the difference between simulated and interferometrically measured cross-correlation between the diffracted field and a reference image of the illumination, using the fast iterative shrinkage / thresholding algorithm (FISTA) framework. We numerically validate this technique by comparing results obtained with spatially incoherent illumination, to those obtained with coherent illumination, and obtain high-resolution reconstructions of similar quality.

As a label-free and quantitative imaging technique, optical diffraction tomography has been widely used in biological imaging. However, it is typically limited to weakly-scattering objects. To overcome this limitation, optimization algorithms based on minimizing field differences at the exit/observation plane, including total variation regularization, have been proposed and demonstrated. We propose a novel optimization algorithm to generalize field discrepancies from one plane to multiple planes throughout the scattering area. We numerically demonstrate that minimizing the field discrepancies at multiple planes instead of only one plane improves the robustness and accuracy of reconstructing multiply-scattering objects, without sacrificing the computational efficiency.

At first the SotA in the field ODT and its applications will be presented. Next the main metrological aspects of tomographic algorithms and holographic tomography systems will be discussed. The comparison metrological parameters of these algorithms and systems (resolution, accuracy of retrieved morphology and absolute RI value) will be provided and discussed based on measurements of the calibrated 3D phase phantom. Also the influence of sample preparation protocols on the quantitative determination of RI is discussed. Finally the efforts towards standardization of holographic data compression for the cases with time laps holographic microscopy and holographic tomography will be introduced.

Fluorescence microscopy has been the workhorse for optical microscopy but this approach confronts several fundamental limits. We show that elastic scattering, which is the basis for the most ubiquitous optical contrast mechanism, provides a very effective avenue for sensitive detection and imaging of nanoparticles and molecules at very high spatiotemporal resolution. We present interferometric scattering (iSCAT) microscopy as the method of choice, explain its theoretical foundation, elaborate on its experimental nuances, and discuss its promise and challenges in the context of applications in detection and tracking of lipids, proteins, viruses and other nanoparticles.

Quantitative imaging of anisotropic dynamics, such as liquid crystal flows, becomes more and more important nowadays . Therefore, there is great demand to develop high-speed quantitative polarization imaging techniques. We propose a novel single-shot, highly accurate and sensitive quantitative polarization imaging technique, called polarized shearing interference microscopy (PSIM), to explore the dynamical properties of disodium cromoglycate (DSCG) under flow at different flow rates. We determine the retardance and orientation angle of DSCG under flow, and show that their spatial and temporal auto-correlation will reduce when the flow rate increases, which is in agreement with the theoretical results based on dimensional analysis of nematic dynamics .

This paper introduces the concept of complex diversity as applied to multiple-wavelength single-shot Quantitative Phase Imaging (QPI). Complex diversity follows from other diversity techniques, like focus diversity and random phase diversity, but unlike the other techniques it includes both amplitude and phase to describe the effective filters used in the reconstruction algorithm. This paper shows that the complex diversity technique significantly outperforms phase-only diversities in terms of the reconstruction fidelity. Experiments are outlined that will demonstrate this technique for QPI.

Structured-illumination (SI) is used for quantitative phase retrieval for improved contrast and sensitivity. However, the nonlinear nature of SI-based phase retrieval process, such as the spatial frequency biases and mixture of different spatial frequency components, usually leads to phase aberrations, in particular in the high spatial frequency components. Recent studies show that nonlinear inversion problems can be efficiently represented by deep neural networks in an end-to-end framework. In this study, we present a deep learning framework for SI-based quantitative phase imaging via the Conditional Generative Adversarial Network (cGANs). A series of structured images paired with the corresponding ground truth of phase images are used to train two competing networks of generator and discriminator. We demonstrate that the GAN-based approach produces sharp and accurate phase image and the structured illumination pattern simultaneously based on our simulation.

Lensfree digital holographic technique can become a powerful microscopic solution by adequately adapting a super resolution(SR) method together with an advanced phase retrieval algorithm. However, it comes at the cost of acquiring multiple images as well as processing large volume of data. Here, we present a multi-height based SR technique that can maximize the signal to noise ratio and the resolution, approximately 2.5 times over the actual pixel size of an image sensor, while minimizing computational cost by utilizing the much less set of the sub-pixel shifted images compared to the conventional SR methods.

We present a combined optical system for Brillouin microscopy with optical diffraction tomography (ODT), which can reconstruct the three-dimensional refractive index (RI) distribution of biological samples. By correlating Brillouin frequency shift with the reconstructed RI distribution in the same field-of-view, we can calculate the precise longitudinal modulus of the samples without a priori information. We demonstrate the capabilities of the method using a cell phantom consisting of different hydrogel beads with known mechanical properties, and apply it to the quantitative characterization of the mechanical and biophysical properties of nuclear compartments inside individual cells under various physiological conditions including cell cycle progression and drug treatments.

We demonstrate a deep learning-based hologram reconstruction method that achieves bright-field microscopy image contrast in digital holographic microscopy (DHM), which we termed as “bright-field holography”. In bright-field holography, a generative adversarial network was trained to transform a complex-valued DHM reconstruction (obtained without phase-retrieval) into an equivalent image captured by a high-NA bright-field microscope, corresponding to the same sample plane. As a proof-of-concept, we demonstrated snapshot imaging of pollen samples distributed in 3D, digitally matching the contrast and shallow depth-of-field advantages of bright-field microscopy; this enabled us to digitally image a sample volume using bright-field holography without any physical axial scanning.

Nanometer-scale deformations of the neuron accompany the action potential. These displacements are measured using a fast quantitative phase microscope and averaged in synchrony with optogenetic stimulation of cultured neurons. The phase movie is further processed by leveraging the spatial and temporal distribution of the spiking signal to detect and segment the separate action potentials in individual cells. An accompanying confocal fluorescence microscopy provides the 3-D cell shape for calibration of the refractive index to calculate the mechanical displacements from the optical phase. Together, these results illuminate the underlying mechanism of the cellular deformations and techniques for achieving all-optical single spike detection.

Although both neurons and oligodendrocytes have been well studied individually, very little is known about how they interact with each other. New methods are needed to further study the intricacies of this interplay in terms of cellular and molecular dynamics. Spatial Light Interference Microscopy (SLIM) is a quantitative phase imaging technique that generates phase maps related to the dry mass content of the sample. In this work, we study the ability of SLIM to quantify myelination at the axonal level. We imaged a series of cocultures comprising hippocampal neurons and oligodendrocytes, of varying densities, using SLIM, and evaluated dry mass formation and growth of myelin.

We demonstrate functional in vivo imaging of photoreceptor and neuronal layers within the living human retina by looking at the expansion of their optical path length. To this end, we use a special full-field swept-source optical coherence tomography system that acquires all lateral points in parallel, achieving a high-speed data acquisition with up to 200 volumes per second. A combination of computational motion and aberration correction with a suitable phase evaluation scheme yields minuscule changes after exposing the photoreceptors to a white light stimulus.

The recent developments in stem cell biology especially the generation of induced pluripotent stem cells (iPSCs) has made possible the development of in vitro cellular models of developmental brain disorders including schizophrenia. Within this framework, we will present how quantitative phase imaging and in particular quantitative phase digital holographic microscopy (QP-DHM), as a label-free technique is able to study these in vitro cellular models and identify both some pathophysiological processes and cell biomarkers related to developmental brain disorders. This will be illustrated by the exploration with QP-DHM of human neuronal networks derived from iPSCs coming from patients suffering from schizophrenia.

We propose to study the behavior of vesicles at the intracellular scale with a non-invasive quantitative phase imaging technology which enables to follow vesicles during a large amount of time without modifying their behavior. We developed specific tools to localize and track cells that we compare to fluorescence-based tracking method. We studied their interaction with the cytoskeleton by inhibiting it using drugs and we implemented statistical tools to quantify and understand intracellular processes at the vesicular level. The resolution and the high sensitivity brought by the High Definition wave front sensor we developed enables to follow all individual vesicles through time follow up, while imaging the whole cell.

The traditional diagnosis of leukemia relies on pathologists to observe and classify cells on bone marrow smears, which is low-throughput, time-consuming, and subject to human bias. To overcome these limitations, we demonstrate intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (OTS-QPI) that acquires bright-field and quantitative phase images of white blood cells (WBCs) containing leukemia cells with high throughput (15,000 cells/s) for deep-learning-based classification. After trained with 64,000 images, a convolutional neural network (CNN) distinguishes three different types of leukemia cells from WBCs with an accuracy of over 96%. Our method provides new possibilities for high-throughput, label-free, and intelligent leukemia diagnosis.

Rapid, label-free, volumetric, and automated assessment in microscopy is necessary to assess the dynamic interactions between lymphocytes and their targets through the immunological synapse (IS) and the relevant immunological functions. However, attempts to realize the automatic tracking of IS dynamics have been stymied by the limitations of imaging techniques and computational analysis methods. Here, we demonstrate the automatic three-dimensional IS tracking by combining optical diffraction tomography and deep-learning-based segmentation. The proposed approach enables quantitative spatiotemporal analyses of IS regarding morphological and biochemical parameters related to its protein densities, offering a novel complementary method to fluorescence microscopy for studies in immunology.

The retina is composed of several transparent layers of neuronal and glial cells active during the vision process. Several studies showed that their structure and density is impacted by numerous eye diseases, such as macular degeneration, glaucoma and retinis pigmentosa. Quantifying such small morphological changes in retinal cells at different depths is of considerable interest to understand the root cause of the diseases as well as to follow the efficacy of new therapeutic approaches. A widespread method to observe the retina ex-vivo at the cellular level is fluorescent confocal microscopy. However, a different imaging technique than fluorescence is required for in-vivo imaging in humans. We have shown that by using a combination of oblique illumination of the retina through the sclera, a phase image of the different layers can be generated and quantified.

Quantitative phase imaging (QPI) has emerged as a non-destructive tool for label-free imaging which helps understand the underlying physiological phenomena [1-7]. In this paper, we establish a correlation between dry mass and the reactivation of Jurkat T-cells latently infected with HIV (JLAT). In a previous study [8], the marker for reactivation of HIV was green fluorescence protein (GFP). A positive correlation between cell diameter and the intensity of viral reactivation was established. Using QPI, we were able to deduce that a positive correlation between dry mass and intensity of reactivation of HIV exists. JLAT isoclone 9.2 cells were treated with tumor necrosis factor (TNF-α) before imaging. Time lapse imaging was done using Spatial Light Interference Microscopy (SLIM) [9-12] with dual channel measurements for phase and fluorescence. Output of the fluorescence channel was quantified for cell diameter and mean fluorescence intensity. SLIM channel provided phase distribution in the sample. This phase information was then used to extract the dry mass [13-15] of cells. Segmentation and tracking were done using MATLAB. Results show that in a cell population with diameter range of 4-28 µm and dry mass ranging from 0.5-3.5 pg, cells having diameter greater than 10 um and dry mass greater than 1.5 pg were reactivated which leads to the conclusion that for the larger cell diameters and dry masses, higher fluorescent intensity and frequency of reactivation events occur as shown in Fig. 1. Thus, through QPI, diameter and dry mass of cells can be used as a marker for reactivation of latent HIV.

Optical diffraction tomography (ODT) is a label-free three-dimensional (3D) microscopy technique allowing to retrieve the refractive-index distributions of optically translucent samples. Until recently, the histopathological applications of ODT have been impeded by the difficulty of imaging thick and wide tissues. Here, we demonstrate rapid, wide-field 3D ODT for imaging biological tissues. We designed a high-speed stitching ODT platform exploiting a digital micromirror device and a motorized stage. Developing a fast algorithmic framework that relieves the stitching artifacts and contrast loss due to multiple scattering, we demonstrate high-contrast 3D imaging of 100-m-thick, square-millimeter-wide pathological tissues at sub-micrometer resolution.

I will discuss our recent efforts in building deep learning based phase imaging techniques that provide improved scalability and reliability. I will demonstrate a physics guided deep learning imaging approach that enables designing highly efficient multiplexed data acquisition schemes and fully leverages the powerful deep learning-based inverse problem framework. We apply this approach to large space-bandwidth product phase microscopy and intensity diffraction tomography, all implemented on a simple LED-array based computational microscopy platform. I will discuss an uncertainty quantification framework to assess the reliability of the deep learning predictions. Quantifying the uncertainty provides per-pixel evaluation of the prediction’s confidence level as well as the quality of the model and dataset.

Histological staining of tissue samples is one of the most helpful tools in diagnosing and prognosing various cancers. However, in order to prepare the slide for a histopathologist to examine, the tissue must first undergo a series of time-consuming processes, such as a staining technique to visually differentiate features in the sample. In this study, we use a label-free method to generate a virtually-stained microscopic image using a single spatial light interference microscopy (SLIM) image of an unlabeled tissue sample, therefore eliminating the need for standard histochemical administration. This novel approach will render histopathological practices faster and more cost-effective, while providing medically relevant dry mass information associated with SLIM images.

Lens-free microscopy aims at recovering sample image from diffraction measurements. The acquisitions are usually processed with an inverse problem approach. Recently, deep learning has been used to further improve phase retrieval results. Here, we propose to alternate iteratively between the two algorithms, to improve the reconstruction results without losing data fidelity. We validated this method for the phase image recovery of floating cells sample at large density acquired by means of lens-free microscopy. This is a challenging case with a lot of phase wrapping artefacts that has never been successfully solved using inverse problem approaches only.

We employ the pseudospectral time-domain (PSTD) simulation to model light propagation through a cluster of dielectric cylinders and investigate the transmission of light. Specifically, we model light propagation through a cornea-like geometry vs. a sclera-like geometry. Simulations show that due to light wave interference, light can be transmitted through a cluster geometry, or, it can be blocked by the cluster geometry, depending on the specific geometrical structure. We explore factors that may contribute to this phenomenon; the simulation findings may provide essential information to analyze this problem.

Computational illumination microscopy has enabled imaging of a sample’s phase, spatial features beyond the diffraction limit (Fourier Ptychography), and 3D refractive index from intensity-based measurements captured on an LED array microscope. However, these methods require up to hundreds of images, limiting applications, particularly live sample imaging. Here, we demonstrate how the experimental design of a computational microscope can be optimized using data-driven methods to learn a compressed set of measurements, thereby improving the temporal resolution of the system. Specifically, we consider the image reconstruction as a physics-based network and learn the experimental design to optimize the system’s overall performance for a desired temporal resolution. Finally, we will discuss how the system’s experimental design can be learned on synthetic training data.

The vast majority of optical imaging methods assume that the incident light is scattered only once before reaching the detector. For thick and/or strongly scattering objects this assumption is not valid. We will describe methods that rely on machine learning and optimization and use examples to produce images in complex media in which multiple scattering cannot be ignored.

Microscopic imaging modalities can be classified into two categories: those that form contrast from external agents such as dyes, and label-free methods that generate contrast from the object’s unmodified structure. While label-free methods such as brightfield, phase contrast, or quantitative phase imaging (QPI) are substantially easier to use, as well as non-toxic, their lack of specificity leads many researchers to turn to labels for insights into biological processes, despite limitations due to photobleaching and phototoxicity. The label-free image may contain the structures of interest, but it is often difficult or time-consuming to distinguish these structures from their surroundings. Here we summarize our recent progress in shattering this tradeoff, by using machine learning to perform automated segmentation on label-free, intrinsic contrast, quantitative phase images.

Off-axis dual-wavelength digital holography (oaDWDH) can enable quantitative phase imaging on thickness samples without numerical phase unwrapping in a single shot. However, the traditional oaDWDH is huge and unstable owing to its separated-path geometries. In this paper, we presented a compact oaDWDH using wavefront-splitting in the quasi common-path. In our approach, a dual-wavelength spherical wave is split into two parts to act as the reference wave and the object wave, respectively. Only a few such optical elements as a mirror and a beam splitter are employed to adjust and recombine the two waves, and a hologram containing two-wavelength information is then captured by a monochromatic CCD camera. The information of a specimen, including phase and height, can be reconstructed through a division algorithm with the help of a specimen-free multiplexed interferogram. In order to verify the feasibility of the system, observations were performed on the step samples. The height of the sample is obtained quantitatively, and finally compared with the measured height result of the step sample by AFM to prove the accuracy of the measurement result.

In this work, we demonstrate a computational microscopy for quantitative phase imaging and refractive index tomography using annular illumination. By employing a physical annular plate or a programmable annular LED unit, the quantitative phase images of a thin phase object or large-volume three-dimensional (3D) refractive index (RI) distributions of thick object can be rapidly characterize and recovered. This annular illumination scheme optimally encodes both low- and high-spatial frequency for quantitative phase and RI information across the entire 3D volume using a small number of intensity measurements. We also give both quantitative phase and 3D RI experiment results based on various biological samples, and this computational microscopy approach shows promise as a powerful high-speed, label-free tool for biomedical applications and possibility of widespread adoption of phase imaging in the morphology study of cellular processes and biomedical community.

Dual-wavelength digital holography has advantages over single-wavelength digital holography in resolving phase discontinuities at high aspect-ratio. However, the operations are very time-consuming and cannot achieve real-time processing. We realized the phase reconstruction of dual-wavelength off-axis holograms on Java platform, and used GPU to accelerate the computation-intensive part. Preliminary experiments show we can reconstruct 1 mega pixel holograms continuously at a speed of 41 fps, which can satisfy the stable video-rate. Through Java, the system can be easily combined with numerous plugins of ImageJ, such as filters, LUT for pseudo-color, 3D tools, etc. This is of great help to the subsequent image analysis and processing.

Digital speckle pattern interferometry (DSPI) has been widely used for surface metrology of optically rough surfaces. Single visible wavelength can provide high measurement accuracy, but it limits the deformation measurement range of the interferometer. Also, it is difficult to reveal the shape of a rough surface with one wavelength in normal illumination and observation geometry. Using more than one visible wavelength in DSPI, one can measure large deformations as well as shape using synthetic wavelength approach. In this work, we will discuss multi-wavelength speckle pattern interferometry using a Bayer RGB sensor. The colour sensor allows simultaneous acquisition of speckle patterns at different wavelengths. The colour images acquired using RGB sensor is split in to its individual components and corresponding interference phase map is recovered using error compensating phase shifting algorithm. The wrapped phase is unwrapped to quantify the deformation or shape information of the sample under inspection. Theoretical background of RGB interferometry for deformation and shape measurements, and experimental results will be presented.

Interferometers are widely used in industry for surface profiling of microsystems. It can be used to inspect both smooth (reflective) and rough (scattering) surfaces in wide range of sizes. If the object surface is smooth, the interference between reference and object beam results in visible fringes. If the object surface is optically rough, the interference between reference and object beam results in speckles. Typical microsystems such as MEMS consist of both smooth and rough surfaces on a single platform. Recovering the surface profile of such samples with single-wavelength is not straight forward. In this paper, we will discuss a dual-wavelength approach to measure surface profile of both smooth and rough surfaces simultaneously. Interference fringe pattern generated on a combined surface is acquired at two different wavelengths. The wrapped phases at each wavelength are calculated and subtracted to generate contour phase map. This subtraction reveals the contour fringes of rough and smooth surfaces simultaneously. The dual-wavelength contour measurement procedure and experimental results will be presented.

Holographic Tomography (HT) is currently the most common tool in biomedical applications, which provides 3D distribution of refractive index (RI) and dry mass (dm) in biological cells and tissues. It uses the refractive index (RI) as contrast agent for a single cell or tissue analysis, which is now considered as important biophysical feature to be applied for future applications in digital phase histopathology or liquid biopsy, e.g., supported by deep learning procedures. RI investigations at cellular level may be performed based on living or chemically fixed cells. In this work we had focused on investigations of the influence of PFA fixation process on RI in cellular organelles: nucleus, nucleolus, nucleoplasm and cytoplasm. The research was carried out on the NRK-52E rat kidney epithelial cell line. Epithelial cells were chosen for the experiment due to the fact that they belong to the basic building cells of the human body and are often used for normal and extended toxicity experiments with nano particles. A commercial Tomocube HT-1S device was used for the research. RI of live and fixed cells was measured. Changes in RI after fixation are observed at the order of 10^-3. The RI values decrease, and the mass density is found reduced in the fixated cells. In conclusion, our results demonstrate the need of standard procedures for the preparation of biological samples for phase tomographic measurements in the case of chemically fixed cells. Moreover the requirement for conversion factors to retrieve accurate RI and dm values that are comparable to living cells is discussed.

Holographic tomography (HT) is a measurement technique utilizing refractive index (RI) as imaging contrast and enabling wide spectrum of applications in modern cell biology. Acquired 3D RI distribution, however, is strongly influenced by the measurement setup and data processing, which calls for reliable tools and methods to characterize and compare metrological parameters of the resulting reconstruction. In this paper we demonstrate and analyze the differences in reconstructions of a 3D-printed test object, which has the optical and structural features of a typical biological (mammalian) cell and has been fabricated at the sufficient level of accuracy for both the geometrical shape and RI distribution metrology. Experimental results have been acquired using commercial and research HT systems and further compared with reference data in an attempt to show the contribution of hardware and software components to the total error. The metrological performance is quantified and discussed in the context of the parameters that are usually of interest during the biomedical interpretation stage such as 3D resolution, volume, RI and dry mass of subcellular structures.

Multispectral quantitative phase imaging (QPI) with digital holographic microscopy (DHM) has been demonstrated to be a versatile tool to access wavelength dependent optical properties of technical and biological specimens as well as to reduce coherence induced image disturbances. However, when hyperspectral DHM is combined with microscopes and tunable lasers with a large spectral range, chromatic aberrations of the optical imaging system have to be considered. We have analyzed wavelength dependent defocusing effects and changes of the optical magnification in the related refocused holographic images in lens-based multi-wavelength DHM and show that both effects can be efficiently numerically compensated.

With increasing demand on data storage and transmission compression has became an important issue in the holographic measurement community. In this work we propose a lossy compression approach designed for the offaxis image plane holograms: a popular type of holograms in digital holographic microscopy and its derivatives. The method utilizes the band-limitation of the signal and is an extension for any conventional image codec. Several codecs are investigated and reliable compression parameters that maintain the holographic measurement precision are presented.

Digital holographic microscopy (DHM) is an interferometry-based variant of quantitative phase microscopy (QPM) that can be integrated modular into various common microscopes for label-free imaging of fast cellular morphology changes in a biomedical laboratory environment. We have utilized a fiber optics-based off-axis DHM concept to monitor the dynamics of beating cardiomyocytes after drug treatment. Our results show that local height changes due to cellular contractions can be detected spatially resolved at the subcellular level. Moreover, we demonstrate that a temporal resolution in the millisecond range is sufficient to detect drug induced increases and decreases of the beating rate.

Quantitative phase imaging (QPI) provides unique access to cellular and subcellular structures with nanometer-scale sensitivity, making it a valuable tool for non-destructive, label-free imaging of biological samples. However, implementation of QPI typically involves a transmission-based geometry and requires thin samples, preventing use of QPI in many important clinical settings, including endoscopy. In this work we demonstrate a fiber-optic device, with epi-illumination, capable of providing quantitative phase information that is well suited for clinical endoscopy, among other biomedical applications.

We propose and demonstrate a high sensitivity common-path quantitative phase microscopy (QPM) technique that can be used to detect nanoscale dynamics with millisecond temporal resolution. Our system is based on a transmission-mode diffraction phase microscope that is implemented with a high electron well-depth camera to reduce the phase noise. Our current system can achieve ~0.1 mrad temporal phase sensitivity, which is one order of magnitude better over most current QPM systems. Our system can be potentially used for observing morphological changes of cells and probing subnanometer membrane dynamics with millisecond temporal resolution.

The measurement and visualization of transient three-dimensional (3-D) physical parameters (density and temperature) distribution of complex flow fields are critical technologies for the characteristics studies of flow fields in modern energy engineering. Among the optical computed tomography (OCT) methods, Moiré tomography has the advantages of simple optical path structure, strong anti-interference ability and wide measurement range, which is especially suitable for complex flow field measurement in noisy environments. Acquiring the transient phase information from the moiré projection is of great importance for the dynamic 3-D parameters reconstruction of complex flow fields. In this paper, the dynamic phase retrieve methods including Fourier and spatial phase-shifting in moiré tomographic are studied, respectively. In the Fourier method, an adaptive first-order spectrum extraction algorithm for Fourier transform moiré fringe and a phase calculation method are proposed. Through this, the projection phase can be obtained directly by multiplying the inverse Fourier transform of the positive first-order spectrum of deformed fringe with the inverse Fourier transform of the negative first-order spectrum of reference fringe. In spatial phase-shifting method, a spatial phase-shifting- interferometry-based moiré volume computed tomography (MVCT) method was proposed to extract the radial shearing phase distribution of grid moiré fringe. The measured results for the first-order partial derivative of the phase projection of a propane flame both by Fourier and spatial phase-shifting methods in the experimental moiré computed tomography systems are presented. The research will be valuable for monitoring the combustion state in energy engineering.