Hong-Ou-Mandel (HOM) interferometry, manifesting as photon bunching at a beamsplitter, can provide high precision optical path length sensing yet is robust to phase noise and immune to phase-wrapping. In this work we demonstrate how the sensitivity of HOM based imaging can be enhanced using dual wavelength illumination whilst maintaining its high level of robustness. We observe a beating in the second order correlation function due to the interference between the two wavelengths bands with a periodicity determined by the wavelength separation. These small-scale features can be used to precisely determine optical path length at the nanometre scale whilst a coarse (micron scale) estimation can be obtained by measuring only one of the bands.

Modern science and technology rely on functional materials, and the physical properties of these materials often strongly depend on defects, local disorder, nanoscale heterogeneities, and grain structures at the atomic scale. Atomic electron tomography (AET) is now able to locate the 3D coordinates of individual atoms and their dynamics with picometer precision and elemental specificity. However, the majority of AET results have been based on incoherent imaging methods, which limit the application of AET for radiation-sensitive materials and light chemical elements. In this talk, I will briefly discuss the current capability of AET and introduce ideas based on quantitative phase imaging to enhance dose efficiencies and enable the detection of light elements [1]. [1]. J. Lee et al., Phys. Rev. Applied 19, 054062 (2023).

The level of computational power we can currently access, has significantly changed the way we think about, process and interact with information. In this talk, I will discuss some of our recent computational microscopy and deep learning work, that showcase some of these shifts in the context of pathology and life science research.

We discuss a coded ptychography technique that significantly enhances imaging resolution and throughput, achieving an order of magnitude improvement over previous methods. Our platform involves translating samples across disorder-engineered surfaces for lensless diffraction data acquisition. These engineered surfaces can be created by smearing a monolayer of blood on the image sensor. By monitoring the phase wraps of the recovered images, we have successfully observed bacterial growth at 15-second intervals over a 120 mm^2 area, achieving phase sensitivity comparable to interferometric measurements. For drug-screening applications, we demonstrate proof-of-concept rapid antibiotic susceptibility testing. By adopting a depth-multiplexed configuration, our approach can be extended to simultaneously image a stack of biospecimens. The fusion of high phase sensitivity, superior spatiotemporal resolution, an ultra-large field of view, and depth-multiplexing capability sets the coded ptychographic microscopy apart from existing imaging techniques, marking an important advancement for high-throughput label-free lensless imaging.

Quantitative phase microscopy (QPM) is pivotal for revealing phase distributions, and optical diffraction tomography (ODT) effectively showcases 3D refractive index (RI) structures across diverse technical and biological entities. Our novel polarization grating-based system, integrated with Hilbert spiral transform (HST) phase demodulation, stands out for its streamlined design, adaptability with partially coherent light, and efficient beam interference through diffraction gratings. In scenarios involving high numerical aperture (NA) paired with low magnification, standard sampling frequently falls short for proficient phase demodulation via Fourier transform. Kramers–Kronig relations enhance phase estimation but face challenges in on-axis configurations as it requires full separation of cross-correlation terms. Our solution, HST with fast iterative filtering, bypasses the need for full term separation enabling phase and amplitude estimation from holographic projections for tomographic reconstruction. Results are validated by 3D RI reconstruction of phase targets 3D printed via 2-photon polymerization and fixed neuron imaging.

Single particle imaging in cryoelectron microscopy (cryo-EM) is a challenging problem due low SNR micrograph data. The standard image processing chain requires extensive averaging of large number of “similar” particle images. A cryo-EM micrograph is an in-line hologram recorded with coherent electron beam where the typical macro-molecules behave as weak phase objects. We show that optical in-line holography methods for auto-focusing can be applied to cryo-EM data to get improved per-particle defocus estimates after including the effect of spherical aberration. Further a sparse iterative algorithm can provide quantitative phase information about single particles leading to a potentially new processing path.

In this contribution, we aim to compare the accuracy of a large variety of quantitative phase imaging (QPI) techniques (phase shifting, off axis, etc) in terms of trueness and precision. For this purpose, we developed a numerical toolbox capable of computing the electromagnetic field at the sample plane of arbitrarily complex microscopes, in the presence of any microscale object at the sample plane. The discussions are based on the numerical imaging of nanoparticles, micro-beads, bacteria, 2D materials and mammalian cells. This way, we could discuss the benefits and limitations of all the existing QPI techniques to help better chose the right tool as a function of the application.

G.Ortolano,A.Paniate,P.Boucher,C.Napoli,S.Soman,S.Pereira,I.Ruo Berchera,M.Genovese The peculiar properties of quantum optical states represent a new resource for innovative imaging schemes [1], as sub-shot noise imaging [2] or quantum illumination [3]. Here, after a general introduction, we present a work where photon number entanglement is exploited to enhance imaging of a pure phase object in a non-interferometric setting. Our method provides the absolute value of the phase without prior knowledge of the object operating in wide-field mode, so it does not need time-consuming raster scanning. Besides a general improvement of the image quality at a fixed number of photons irradiated through the object, we demonstrate a clear reduction of the uncertainty in the quantitative phase estimation. Significant application, in particular in biology, can be envisaged. [1] M.Genovese, J.of Optics, 18 (2016) 073002 [2] G.Brida et al., Nat.Phot. 4, 227 (2010). N.Samantaray et al. Light: Sc.&App.(2017) 6, e17005 [3] E.Lopaeva et al.,PRL 110, 153603 (2013)

We propose a novel approach for reflective measurements using utilizes high temporal coherence and low spatial coherence illumination in a full-field reflective geometry. This allows the reference plane to be shifted to the camera, eliminating the need for meticulous alignment and enabling the use of a single high numerical aperture (NA) objective lens. To enhance the quality of our measurements, we perform deconvolution and digital aberration correction on the measured data. We achieve a resolution close to the theoretical limit of our system: 100nm-100nm-500nm and demonstrate the unique capabilities of our experimental setup on both biological and industrial samples.

Conventional wavefront sensors suffer from the fundamental limitation of the space-bandwidth product, and have a trade-off between their spatial sampling interval and dynamic range. Here, we leverage nonlocal thin film optical filters with optimized angle- and polarization-dependent response to circumvent the fundamental limitation, and realized a robust single-shot wavefront sensing system with a small spatial sampling interval of 6.9 μm and a large angular dynamic range of 15°. The system only requires inserting two multilayer dielectric filters, fabricated using a mature thin film deposition technique, into a conventional 4-f imaging apparatus. The polarization-sensitive nonlocal filters are used to map the 2D phase gradients of the incident light field to the intensity variation of the x- and y-polarized light, respectively, thus enabling single-shot 2D wavefront reconstruction from images taken by a polarization camera. Such a system may be used for a variety of applications, including high-resolution image aberration correction, surface metrology, and quantitative phase imaging.

With the recent development of X-ray sources with high coherence, such as free-electron X-ray lasers (XFELs) and diffraction-limited storage rings, new possibilities have emerged for applying the advanced phase imaging methods to X-rays. In this talk, I will briefly introduce various phase-measuring methods in the X-ray regime with their pros and cons, and some of the recent advances in phase imaging methods with their successful applications to X-rays at Pohang Accelerator Laboratory.

We demonstrate a single-shot full-field reflection-mode off-axis quantitative phase microscopy with temporal focusing. The new imaging system is capable of achieving diffraction-limited optical sectioning quantitative phase imaging without any spatial or angular multiplexing. It has high potential to achieve ultrafast imaging with camera-limited frame rate. We show that the QPM can measure the depth-dependent intracellular scattering dynamics of cells and reveals 3D structure of tissues with different phenotypes at sub-cellular resolution.

Quantitative phase profilometry (QPP) has been recently developed for mapping the thickness profiles of thin films. However, the insufficient signal-to-noise ratio (SNR) has hindered its applicability on thinner structures, e.g., monolayer graphene. Here, we propose and implement a “phase cavity” concept in QPP to amplify the measured phase signal to achieve high contrast and accurate thickness profiling of thin structures. We achieved phase amplification by modulating the oxide film thickness on the silicon substrate and optimizing the illumination wavelength. We obtained the exact thickness profile of monolayer graphene and confirmed it with Raman spectroscopy and atomic force microscopy.

This talk will describe new microscopy methods for imaging of thick scattering samples in both transmission and reflection mode. We use computational imaging techniques to measure 3D phase (refractive index) in samples that incur multiple scattering (e.g. embryos or whole organisms), by modeling the scattering and solving an inverse problem with machine-learning augmented inverse solvers.

In optical imaging, this presentation unveils advancements in two key areas: quantitative phase imaging (QPI) cell segmentation and QPI's application in targeted enzyme activity mapping. QPI complements fluorescence imaging, revealing cell morphology and dynamics without contrast agents. Live-cell 3D tomography enables systematic parameter analysis, free from phototoxicity. Yet, 3D tomogram analysis lags behind imaging speed. Introducing CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging), an algorithm that achieves rapid single-core cell segmentation. Addressing clumped cells and interferogram drifts, CellSNAP empowers high-throughput analysis, expanding QPI adoption. Beyond this, QPI's potential in molecular imaging, particularly enzyme activity mapping, is explored. Innovative QPI contrast agents reveal enzyme activity via refractive index modulation, with implications for disease diagnosis and therapeutic assessment.

A colloidal membrane is one rod-length thick fluid monolayer that spontaneously assembles in the presence of a non-adsorbing polymer. Including the transformation from a flat 2D colloidal membrane to an edgeless 3D colloidal vesicle, colloidal membranes undergo a range of remarkable morphological and topological shape changes, yet quantitative insight into such dynamics is limited due to the limitations of the existing imaging techniques. Here, we present a study on the dynamics of colloidal membranes by employing dielectric tensor tomography (DTT). By recording diffracted light from colloidal membranes for various illumination angles, the principles of DTT enables the reconstruction of the 3D tomograms of directors and principal refractive indices with high temporal resolution without fluorescence labeling or mechanical scanning for 3D imaging. Using the technique, we investigated the forming and closing dynamics of transient pores on colloidal vesicles in response to changing osmotic pressure, and the orientational change of constituent rod particles around the pore.

Immunothrombosis is a critical aspect affecting patients in acute care settings, especially in conditions involving infection, trauma, or severe inflammation. The interplay between hemostasis, innate immunity, and inflammation becomes highly relevant in these scenarios and can significantly impact patient outcomes. Blood cell aggregates are potential functional cellular biomarkers for prognostic and predictive biomarkers of immunothrombosis but require, due to the low logistical stability of the aggregates, a point-of-care (POC) solution for standardizable diagnostics. Here, we present a POC-compatible method that combines a quantitative phase imaging method with a microfluidic chip and a customized image analysis, resulting in a label- and sample preparation-free high-throughput imaging flow cytometer. We optimized our conditions to mimic the flow conditions in vessels at a low shear stress of ~1.000 s−1. In the acute care uses case, we demonstrate the dynamics of blood cell aggregates in critical care patients and discuss the potential impact on clinical workflows.

The cell interior is highly compartmentalized into various biomolecular condensates. The biophysical properties of condensates are thought to be tightly linked to their functions, yet a deeper understanding of this relationship is hampered by a lack of tools to quantitatively probe the organization of condensates in living cells. Here, we use optical diffraction tomography (ODT) to measure the biomolecular density of condensates. We find that intracellular condensates exhibit a broad range of biomolecular densities. The low-density ones exhibit densities similar to the surrounding cyto- or nucleoplasm, and are highly permeable to cellular protein probes. We also uncover that RNA plays a key role in tuning the density of condensates. Our work highlight that ODT is a powerful tool for exploring the complexity of the intracellular phases.

Optical diffraction tomography has recently emerged as a promising technique for obtaining morphological and biochemical information, such as lipid and protein concentrations, from individual cells. Herein, we propose a quantitative phase imaging application that enables the measurement of morphological and physiological parameters in asthma-specific immune cells without the need for exogenous labeling agents. By analyzing the reconstructed 3D refractive index distribution, our technique provides quantitative data on asthma-specific cell physiology. The potential of these imaging-based biomarkers as tools for characterizing phenotypical changes in immune cells may support the pursuit of early diagnosis and a comprehensive understanding of asthma diseases.

The Nucleophosmin 1 (NPM1) mutation rapidly progresses to acute myeloid leukemia, emphasizing the need for early diagnosis, especially in cases with low blast counts. Some low blast count instances may not undergo next-generation sequencing, causing delays and necessitating swift NPM1 mutation screening. This study utilizes cutting-edge label-free three-dimensional imaging with holotomography (HT) to identify NPM1 mutation in blasts. Machine learning and deep learning algorithms achieve precise single-cell and patient-specific predictions. HT's accurate detection of protein movement associated with NPM1 mutation holds great promise as a reliable and efficient tool for early detection in hematologic malignancy patients with low blast counts.

Spatial biology provides unprecedented insights into cellular function within microenvironments, crucial for precision medicine. Yet, many commercial imaging systems, largely limited to fluorescence imaging of fixed samples, miss out on intrinsic cellular dynamics and are laborious with extended data acquisition, limiting their widespread use. We introduce a multiscale multimodal imaging platform that integrates quantitative phase, hyper-plex fluorescence imaging, and vast field of view at sub-micron precision. This platform also co-registers molecular-scale super-resolution images, linking molecular data with mesoscale cellular contexts. It can capture dynamic cell morphologies instantly, revealing cell states and molecular intricacies. With macro-scale optics and an astronomy camera, combined with an automated processing pipeline, our system delivers high-resolution imaging across mesoscale. We demonstrated its value in studying cancer cell resistance to chemotherapy, embracing a multi-scale, multimodal approach. Ultimately, this tool will enable profound insights into cell environment, heterogeneity, morphological changes, and molecular information across vast cell population.

Optical diffraction tomography of biological cells is an emerging label-free sensing technique. It is promising for studies of cellular functions towards understanding the causes of diseases. Illumination scanning is Commonly used, which enables accurate calibration of the projection angle. However, as a trade-off the technique suffers from the missing cone problem. We present an AI-driven alternative using precise adaptive-optical cell-rotation. A multi-core fiber is transformed to a remote phased-array, employing a spatial light modulator and a novel phase encoder neural network called CoreNet. The resulting high-fidelity light-field delivery enables targeted 3D cell-rotation resulting in full spatial frequency coverage. The cell motion and rotation angle are detected automatically and in real-time by a workflow based on machine learning and computer vision leading to rapid and robust tomographic reconstruction. The multicore fiber endoscope enables keyhole access for cell rotation tomography of HL60 human cells for cancer studies.

We apply our recently developed QPI methodologies, which quantify and track intracellular water content, to investigate the role of aquaporin (AQP) proteins in pulsed electric field (PEF) induced water uptake in cells. QPI imaging is performed on Jurkat and glial cells subjected to PEF simulation. The effects on the transmembrane water flux of the cells are investigated when solutions contain mercury, a known broad spectrum AQP blocker, or commercially available AQP blockers. Our study demonstrates the utility of QPI for rapid intracellular water quantification and shows a path forward for identifying biophysical mechanisms using label-free imaging.

Induced pluripotent stem cells (iPSCs) hold the potential for personalized regenerative medicine. Yet accurately gauging the stemness of each colony remains a challenge since existing methods are inconsistent or harmful to iPSCs. Addressing this, we introduce holotomography (HT), a non-invasive microscopic technique. HT, through three-dimensional refractive index distributions, revealed iPSC structures at various scales as well as properties like volume, mass density, and lipid ratio. We identified altered properties in iPSCs exposed to differentiation agents, and then employed a machine-learning algorithm to detect reduced stemness from images. Through these results, HT emerges as a potential tool for iPSC quality maintenance.

Simultaneously delivering specificity, high throughput, and sensitivity has been one of the long-sought goals in label-free all-optical approaches. Particularly in the context of sensing, accurate characterisation of heterogeneous nanoparticle populations demands methods that comply with these three key properties at the single particle level. Nevertheless, this poses a significant challenge to date simply because the smaller the size of a particle, the greater the demand on sensitivity, which usually is paid in the currency of throughput. Similarly, the greater the number of markers used to provide specificity, the lower the throughput. Here, I will introduce a series of optofluidic platforms developed in the group that uniquely fingerprint heterogeneous nanoparticle suspensions by integrating digital holographic microscopy with microfluidics and surface chemistry.

During the last several decades, cephalopods have emerged as a powerful source of inspiration for the engineering of dynamic optical systems. Previously, we drew inspiration from the tunable optical functionality of cephalopod skin cells for the engineering of human cells to produce high refractive index subcellular architectures consisting of the cephalopod protein reflectin. Additionally, we investigated the self-assembly, optical properties, and stimuli responsiveness of various reflectin isoforms in-vitro, utilizing three-dimensional holotomographic microscopy. More recently, we have obtained improved control over the production of reflectin in human cells by genetically engineering cell lines to stably express reflectins. Furthermore, we have systematically interrogated the optical properties of reflectin-expressing cells with three-dimensional holotomographic microscopy and harnessed their high-contrast reflectin subcellular structures to track cell migration (2,3). The combined findings have indicated that reflectins could find applications as genetically encoded high-contrast biomolecular reporters for the study of cellular-level processes.

Our research focuses on melanocytic neoplasms, a diverse range of skin tumors, with distinct etiologies that impact patient morbidity and mortality. We pioneer approaches using Quantitative Phase Imaging (QPI) on fresh human biopsies to explore melanocyte cell biology. Our methods have uncovered new roles for oncogenic mutations and revealed therapeutic approaches for understudied malignant melanoma subtypes. The insights from QPI exhibit high consistency in pre-clinical systems. Our presentation will highlight QPI's instrumental role in our discoveries and its contribution to two upcoming studies.

With applications in pathogen screening, cancer cell classification, and digital pathology, quantitative phase microscopy (QPM) quantifies optical phase differences to yield information about the morphology, dry mass, and dynamics of biological specimens. Being a label-free physical measurement, QPM is well suited to be integrated with modern physics-based machine-learning approaches. To this end, we present end-to-end differentiable physical and analytical neural models to build optimal QPM solutions for target applications. We further discuss the interpretability of our models from a statical viewpoint to critically evaluate their performance beyond simple accuracy tests.

We present an all-optical diffractive processor that can perform snapshot multispectral quantitative phase imaging (QPI) of transparent, phase-only objects. By leveraging multiple layers of spatially-structured diffractive materials designed via deep learning, this all-optical processor can transform phase profiles of an input object at various spectral bands into desired spatial intensity variations measured by a monochrome focal plane array to rapidly obtain the multispectral QPI of the object. This framework provides a compact and power-efficient multispectral QPI solution that can operate at different parts of the electromagnetic spectrum by integrating these thin diffractive layers on top of existing image sensors.

We present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity non-destructively on unlabeled specimens. ACM is equipped with a laser scanning confocal microscopy with a quantitative phase imaging module, which provides optical path-length maps of the specimen colocalized with the fluorescence channel. Using pairs of phase and fluorescence images, a convolution neural network was trained to translate the former into the latter. The ACM images hold much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids.

Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications.

Recently, various optical neural architectures have been designed to perform imaging and all-optical image processing. These designs consist of several optical masks with optimized transmission coefficients for the task. Designing sparse optical masks for them is important as it can promote different aspects of the design such as ease of fabrication and power efficiency of the design. To this end, inspired by the sparse filter designs, we propose training optical neural architectures with a regularization promoting sparsity in the masks. As preliminary results, we demonstrate a D2NN design for QPI achieving a sparsity of 33% with a performance degradation of only 12%.

Hematoxylin and eosin (H&E) staining has been a gold standard for diagnosing cancer in histopathology. Nonetheless, H&E staining heavily requires time, resources and is limited in two-dimensional analyses. Here, we propose three-dimensional (3D) virtual staining of hematoxylin and eosin (H&E) from label-free refractive index (RI) images of histopathological slides. To achieve this, we integrated RI tomography, which provides 3D RI distribution without staining, with deep learning. By predicting brightfield (BF) images from RI images of thick colon slides, we generate virtually stained 3D H&E images. The accuracy of our approach is validated against conventional staining methods.

Quantitative oblique-back-illumination microscopy (qOBM) enables quantitative phase imaging (QPI) with epi-illumination, and thus permits the use of phase contrast in applications that were previously out-of-reach for QPI, including clinical medicine. Here, I will discuss our latest efforts to apply qOBM for clinical applications, specifically tissue imaging for non-invasive diagnostics and image guided therapy. Our approach uses an unsupervised cycle generative adversarial networks to translate 3D phase images of thick fresh tissues to appear like H&E-stained tissue sections. This work paves the way for non-invasive, label-free, real-time 3D H&E imaging which can be transformative for disease detection and guided therapy.

We developed a flexible endomicroscopy (FEM) in reflection geometry for quantitative phase imaging of unlabeled thick samples. FEM provides the phase information by wavefront modulation using chessboard grating and phase-shifting. FEM system features high lateral and axial resolutions of 1.2 and 8.3 μm, respectively, with a probe diameter of 2.5 mm. By testing pathologic slices and thick opaque mammalian tissue, FEM identifies normal and tumor glandular structures, secreta, and tomographic skin layers. With the potential for direct morphological and phase measurement, high resolution, and thin fiber tip, the label-free FEM could be an attractive tool for various clinical applications.

In-vivo imaging of the retina inevitably requires an image acquisition through the subject's pupil, which suffers from aberrations induced by the subject's crystalline lens and cornea, as well as scattering by tissue. Epi-illumination differential phase contrast (DPC) has demonstrated recovery phase information of cellular structures from intensity images captured with oblique back-illumination; but its image quality can still be compromised by eye movements and aberration. To overcome these challenges, we propose a computational method that compensates for the aberration in the platform of Oblique back-illumination differential phase-contrast microscopy. Our method involves asymmetric oblique-back illumination, so that phase images can be obtained with smaller number of acquisitions. The acquired images are then processed with a novel gradient decent-based algorithm to obtain aberration-corrected images and pupil aberration simultaneously. We will present the details of our experimental setup and reconstruction algorithms.

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 takes into account the spatiotemporal image priors. In particular, total variation with respect to the complex spatio-temporal datacube is introduced as a sparsity-promoting regularizer. The phase retrieval process is formulated as a standard optimization problem and is solved via an accelerated proximal gradient method. The algorithms are evaluated on a proof-of-concept QPI imaging system based on defocus diversity. Numerical and experimental results both indicate that the proposed spatio-temporal compressive phase retrieval framework could achieve high-fidelity quantitative phase imaging while improving the temporal resolution to that of a single-shot method. We experimentally demonstrate video-rate QPI of dynamic biological activities that is free of motion blur and twin-image artifacts. The proposed framework could potentially achieve a high space-bandwidth-time product and push the information throughput of QPI systems towards the theoretic limit.

Off-axis digital holographic microscopy (DHM) provides both amplitude and phase images, and so may be used for label-free 3D tracking of micron- and submicron-sized particles of different compositions, including biological cells, strongly absorbing particles, and strongly scattering particles. Contrast is provided by differences in either the real or imaginary parts of the refractive index (phase contrast and absorption) and/or by scattering. While numerous studies have focused on improving resolution in DHM, particularly axial resolution, there has not been studies quantifying the limits of ability to detect and track unresolved particles. This measurement has important implications for microbial detection, including in life-detection missions for space flight. Here we examine the limits of detection of nanosized particles as a function of particle optical properties, microscope optics (including camera well depth and substrate), and data processing techniques.

We present a deep label-free quantitative phase imaging method using confocal gradient light interference microscopy (CGLIM) for studying ex vivo three-dimensional cellular clusters and in vivo mouse brains. CGLIM is a custom-built confocal reflectance differential interference contrast (DIC) microscope with phase-shifting interferometry using long-wavelength (~1.7 μm) illumination. As a laser-scanning common-path interferometric technique based on linear scattering, CGLIM has superior stability and sensitivity in phase measurement and reduces photodamage, or heating to living systems. With the proposed method, we will demonstrate the working principle on standard samples, and the phase imaging of cancer spheroids and in vivo mouse brains.

The flagellum is vital for eukaryotic cell survival. We propose method utilizing digital holographic microscopy (DHM) advantages, such as high-speed imaging and precise axial localization, to develop a versatile approach for four-dimensional tracking (X, Y, Z, time) of eukaryotic microorganisms' bodies and flagella, addressing challenges faced by existing methods. We reconstructed for the first time the shape of a 200 nm diameter Chrysochromulina simplex flagellum and measured mouse sperm flagella over time, capturing approximately 800 points. Our technique opens new avenues for studying flagella's roles in cellular functions and survival strategies, offering high-speed and precise 3D tracking at the nanoscale.

Optical anisotropy is an intrinsic property of many materials which originates from the structural arrangement of molecular structures. Various polarization-sensitive imaging (PSI) methods have been developed to visualize and quantify the birefringence of the optically anisotropic materials. Recently, volumetric imaging techniques were introduced to investigate the three-dimensional (3D) anisotropy distribution. However, those 3D PSI methods are mostly based on single-scattering approximation, which are not suitable for optically complicated, multiple-scattering 3D objects. In this presentation, we present a novel non-interferometric 3D polarization-sensitive computational imaging technology—polarization-sensitive intensity diffraction tomography (PS-IDT) —that enables the reconstruction of 3D anisotropy distribution of both weakly and multiple scattering specimens from intensity-only measurements. We demonstrate the 3D birefringence imaging capabilities of PS-IDT by presenting 3D anisotropy maps of various samples, including potato starch granules and tardigrade.

The transport of intensity (TIE) technique allows for label-free quantitative phase imaging by acquiring two or more images from different focal planes. However, current TIE systems can be limited in speed or accuracy by methods used for the control and selection of the focal planes. Here, we introduce a novel microscope that combines an acousto-optic liquid lens (TAG lens) with illumination engineering to achieve precise electronic defocus control, indistinguishable from traditional mechanical defocusing, at microsecond timescales. Such high speed enables phase-imaging of dynamic events, including an evaporating droplet or a moving microorganism, at diffraction-limited resolution and rates of up to 150 Hz.

Conventional imaging apparatus, composed of lenses and camera, are limited to recording light intensity. Nevertheless, the phase of light has the potential to transmit more information than its intensity. Unfortunately, the acquisition of phase information generally demands the incorporation of sophisticated and unwieldy optical elements. Here, we propose a single-shot complex amplitude imaging meta-optics system capable of capturing both amplitude and phase information of the light field. By leveraging the versatility and adaptability of metasurface, a compact imaging configuration, comprising of just one single-layer metalens and one off-the-shelf polarization camera, can uniquely determine phase and amplitude from the shearing interference patterns in the four polarization channels based on polarization phase-shifting method. We experimentally demonstrate a variety of applications including surface topography measurement, metasurface phase characterization and quantitative phase microscopy with high accuracy. The results showcase promising and potential advancements in miniaturized complex amplitude imaging systems and portable applications .

Quantitative phase imaging is the representative of state-of-the-art marker-free full-field optical metrology techniques. The inPhaseNet is a phase demodulation algorithmic solution, where convolutional neural network was used for the quadrature function (input fringe pattern shifted in phase by π/2) retrieval. Having both neural network input and output data phase function is calculated via arctangent function. Since phase demodulation consists a very important part in a measurement process its algorithm imposes one of the main limitations of the entire QPI unit. Phase decoding results are favorably compared with reference algorithms, i.e., classical Variational Hilbert Quantitative Phase Imaging, the Hilbert-Huang and Fourier transforms.

The silicon platform has been widely applied in optoelectronics and sensing to attain versatile functions. Epi-mode quantitative phase imaging (QPI) is promising for non-destructive and high-throughput material metrology. However, spatial resolution and its influencing factors have not been fully explored in epi-mode QPI. Here, we propose to explore the spatial phase resolution limit by studying the influencing factors, including object shape, object size, and phase noise. These factors are related to the signal-to-noise ratio (SNR). The phase object distribution is solved from the transfer-matrix model combined with the consideration of the limited aperture of the imaging system. The study may inspire researchers to further enhance the resolvability of QPI by improving image SNR.