This PDF file contains the front matter associated with SPIE Proceedings Volume 11653, including the Title Page, Copyright information, and Table of Contents.

Introduction to SPIE Photonics West BiOS conference 11653: Quantitative Phase Imaging VII

We proposed a fast 3D RI construction method, based on the Wolf equations for propagating correlations of partially coherent light. This approach, referred to as Wolf phase tomography (WPT), involves minimal computational steps, renders high-resolution RI tomograms, without time-consuming deconvolution operations. WPT decouples the refractive index distribution from the thickness of the sample directly in the space-time domain, without the need for Fourier transformation. We demonstrate that, from three independent intensity measurements corresponding to each phase shift, the RI distribution is reconstructed directly from the Laplacian and second time derivative of the complex correlation functions.

We propose a multimodal imaging system, LS-GLIM, consisting of the light scanning microscope (LSM 900, Zeiss) and the gradient light interference microscope (GLIM) module [1], with photomultiplier tube (PMT) detection. GLIM upgrades a differential interference microscope with a liquid crystal variable retarder (LCVR), which introduces further controlled phase shifts between the x and y polarized light. The quantitative phase information can be retrieved from the four frames corresponding to each phase shift in the GLIM. We anticipate a broad range of applications that LS-GLIM will enable through phase imaging with computational specificity (PICS), especially in thick, highly-scattering samples.

Optical metasurfaces enable control of the phase, polarization and amplitude of optical wavefronts with high spatial resolution that can be difficult to achieve via other means. This opens the possibility of developing new imaging devices that provide various advantages in terms of size and multi-functionality compared to conventional optical systems. One such modality is quantitative phase imaging, that enable label-free imaging of transparent bio-samples. Miniaturization of these imaging systems will extend their potentials in biomedical and diagnostic applications. We demonstrate a novel quantitative phase gradient microscope using two multifunctional metasurface layers that retrieves a quantitative phase gradient image in a single shot with low noise levels and single cell resolution. Besides quantitative phase imaging, I will discuss other imaging applications for dielectric metasurfaces, including hyperspectral imaging.

Lensless holographic imaging is challenged by the twin-image artifact due to the missing phase and the aliasing effect due to the undersampled measurement. Therefore, phase retrieval and pixel super-resolution (PSR) techniques serve as the essential ingredients for high-fidelity holographic imaging. In this work, we combine the two in a unified framework by formulating the PSR phase retrieval as a non-convex feasibility problem. An adaptive smoothing strategy for escaping local minima is introduced. Numerical and experimental results are presented and discussed. The proposed framework can be generalized to various physical settings, and is compatible with the state-of-the-art iterative projection algorithms.

A quantitative phase microscope with improved accuracy and extended field-of-view (FOV) is demonstrated based on coded aperture correlation holography. By using a certain type of coded phase masks, we increased the phase measurement certainty up to a factor of 5 compared to a conventional, open-aperture interferometric approach. Moreover, a threefold FOV extension is shown based on the same type of coded phase masks, independently of the area ratio between the used phase mask display and the image recording devices. Integration of the two powerful imaging capabilities in a single camera exposure turns the suggested approach to an attractive phase imager.

We propose synthetic aperture gradient light interference microscopy (SA-GLIM) as a solution to avoid computational complexity in standard Fourier pytchographic microscopy. This new system combines direct phase measurements from GLIM with various illumination angles, and a synthetic aperture reconstruction method, to produce high resolution, large FOV quantitative phase maps. Using a 5× objective lens (NA = 0.15), SA-GLIM generates phase maps with a spatial resolution of 850 nm and FOV approximately 1.7×1.7 mm2. We tested the performance using a mixture of polystyrene beads (1 μm and 3 μm in diameter), and the smaller beads can be easily resolved in the final image. Compared with standard FPM, SA-GLIM records substantially fewer low-resolution images, which makes the data throughput highly efficient.

In digital holographic microscopy (DHM) scattering patterns and parasitic interference fringes due to internal reflections that are caused by coherent laser light affect quantitative phase imaging (QPI). We present an approach for the reduction coherence induced disturbances. Therefore, the sample illumination is modulated by an electrically focus tunable lens while a series of digital off-axis holograms is recorded from which subsequently averaged QPI images are retrieved. The principle is compatible with Mach-Zehnder interferometer-based off-axis DHM and capable to be integrated into commercial research microscopes. The performance of our approach is demonstrated by results from living porcine epithelia cells and mouse fibroblasts.

Limited angle optical diffraction tomography (ODT) is a 3D quantitative phase imaging method that allows to retrieve information about 3D refractive index (RI) distribution of live, unlabeled biosamples. The main limitation of this method is that its common transmission configuration results in very low axial resolution. On the other hand, optical coherence tomography (OCT), working in its most popular reflection configuration retrieves information about the gradient of the RI of investigated samples. However, the results are of qualitative nature. Moreover, due to low numerical aperture of the objective lens typically used in OCT systems, the resolution is high in the axial direction and relatively low in transverse direction. From the point of view of K-space filling, these two imaging modalities are complementary. Here we present a method of combining ODT projections with OCT scans. The combined technique, called optical coherence diffraction tomography (OCDT) operates in transflective mode, where ODT is captured in transmission and OCT in reflection. Theory behind conversion of OCT scans into ODT projections is given. With the use of numerical simulations we show what enhancement can be obtained when OCT and ODT data are combined directly. Also, experimental verification is presented.

We present a deep learning-enabled holographic polarization microscope that only requires one polarization state to image/quantify birefringent specimen. This framework reconstructs quantitative birefringence retardance and orientation images from the amplitude/phase information obtained using a lensless holographic microscope with a pair of polarizer and analyzer. We tested this technique with various birefringent samples including monosodium urate and triamcinolone acetonide crystals to demonstrate that the deep network can accurately reconstruct the retardance and orientation image channels. This method has a simple optical design and presents a large field-of-view (>20-30mm2), which might broaden the access to advanced polarization microscopy techniques in low-resource-settings.

Quantitative Phase Signal (QPS) stems from the difference between the refractive indices of the observed specimen and that of its surrounding environment (ne). Therefore, any change in ne will drastically affect the QPS. We have developed an original approach, based on both the manipulation and the measurement of ne that allows from the QPS the visualization of various structures inside the observed specimen as well as accurate measurements of their respective refractive indices. Such an approach performed with digital holographic microscopy on living cells reveals internal cell structures including organelles (nucleus, nucleolus, vesicles, etc.) and some components of the cytoskeleton.

A transmission-mode high sensitivity quantitative phase microscope (QPM) is developed for profiling transparent thin film structures. The QPM system is implemented with a common-path interferometry design and a high well-depth camera, which has allowed us to achieve an optical path length difference sensitivity of around 50 picometers. A frame averaging method can be used to further improve the sensitivity. To account for multiple interference within thin films, a transmission matrix model is developed to achieve accurate height profile reconstruction. With the correction model, the profiling accuracy can be improved from 20.6% to 4.0% for a MoS₂ thin film with a thickness of around 25 nm.

Ultraviolet hyperspectral interferometric (UHI) microscopy is a novel molecular imaging technique that retrieves wideband, high-resolution spectral information of the wavelength-dependent real part of the refractive index (i.e., dispersion), and the total attenuation coefficient of biological samples in the deep-ultraviolet (UV) spectral region. Here I will discuss the design of the system and processing methods used to extract the hyperspectral information. I will also present results from individual cells, tissues, and a detail study of spectral properties of several important biomolecules. Finally, biomedical applications and future directions will be discussed.

We present a deep learning approach for the rapid resolution enhancement of optical diffraction tomography. Once our three-dimensional U-net-based convolutional neural network learns an image translation between raw tomograms and total-variation-regularized tomograms, the trained network can fill in the missing cone of a measured refractive index tomogram and improve its resolution within seconds. We demonstrate the feasibility and generalizability of our approach on various biological samples, including bacteria, WBC, and NIH3T3.

LED array microscopy is an emerging platform for computational imaging . Existing LED array systems often exploit transmission imaging geometries of standard brightfield microscopes that leave the rich backscattered field undetected. Here, we develop an LED array reflectance microscope capturing the sample’s backscattered signal. In particular, we demonstrate multimodal brightfield, darkfield, and differential phase contrast imaging on fixed and living biological specimens including Caenorhabditis elegans (C. elegans), zebrafish embryos, and live cell cultures. Video-rate multimodal imaging records real-time features of freely-moving C. elegans and the fast beating heart of zebrafish embryo. Our new reflectance mode is a valuable addition to the LED array microscopic toolbox.

We report the integration of quantitative-phase imaging (QPI) with light-sheet (LS) fluorescent microscopy on to a standard inverted microscope that retains compatibility with microfluidics. QPI enables label-free imaging and number-density quantification of single cells and their organelles. Conversely, LS yields considerable speed and phototoxicity gains in quantifying the 4D dynamics of gene-encoded fluorescent biomarkers. We will detail the system design that relied on spatial light interferometry for QPI and an accelerating Airy-beam light-sheet for fluorescence, its performance, as well as results of a representative multivariate imaging analysis of single-cell metabolism.

We present a novel dual-beam optical trap for multi-dimensional cell-rotation. A paradigm shift for refractive index tomography is achieved through wavefront shaping using multicore fibers (MCF), resulting in adaptive phased arrays. The amplitude and phase of each core can be independently controlled, enabling the delivery of tailored light-field distributions to control the cell orientation in the region of interest. Using in situ calibration and on-the-fly manipulation enables precisely controlled cell-rotation around multiple axes in three-dimensions, leading to full spatial frequency coverage and overcoming of the “missing apple-core”. This upgrades the performance of the three-dimensional refractive index distribution measurement of the cell.

Combining synthetic aperture approaches with reference-less setups, ptychography is a promising phase retrieval technique for label-free quantitative phase imaging. Within the phase retrieval community, spectral methods are known to accelerate gradient descent schemes, however their positive effect on experimental ptychographic datasets has not been proved. Inspired by the latest theories on optimal spectral estimation, we achieved 3 times faster ptychographic reconstructions than with a standard gradient descent algorithm, in both simulations and experiments. The algorithms and experimental parameters crucially impacting the convergence speed are discussed. We believe that spectral methods will help improve both theoretical understanding and experimental implementations of ptychography.

We present a novel optical coherence microscopy (OCM) implementation which permits accurate measurements of intensity and phase of light returned from the sample. We demonstrate the capacity to acquire stable phase recordings with a phase sensitivity of 0.1 nm rms at 500 Hz frame rates with a field-of-view of 250x250 µm and 1 µm lateral resolution. Instantaneous, coherence gated, quantitative phase imaging at high frame rates enables observations of the dynamics in cells and tissues, providing instant feedback on metabolic activity and tissue states. We demonstrate applications of the technology for ultrasensitive measurement of intracellular activity and cellular dynamics in cell cultures.

The reconstruction of an object hidden behind a scattering curtain is a modern topic in the field of imaging, which has stimulated an active scientific production over the past few years. However, most of the work done in the field was in addressing the reconstruction of a bi-dimensional object. Here, instead, we tackle the reconstruction of a three-dimensional fluorescent sample hidden behind an opaque layer. To do so, we show that the auto-correlation operation well behave in projection tomography, letting us to reconstruct a three-dimensional auto-correlation of the object. By having access to such information, it is possible to implement a phase retrieval algorithm to roll back to the actual reconstruction of the specimen.

The phase sensitivity limit of Differential Phase Contrast (DPC) with partially coherent light is analyzed in details. The main optical parameters, such as the shape of illumination and the numerical aperture of the objective, together with the noise of the camera used to determine the minimum phase that can be detected. We found that a priori information about the sample can be used to fine-tune these parameters to increase phase contrast. We propose a simple algorithm to predict phase sensitivity of a DPC setup, which can be performed before the setup is built. Experiments confirm the theoretical findings.

3D differential phase contrast (3D DPC) microscopy uses asymmetric illumination patterns and axial scanning to recover volumetric maps of refractive index. To avoid the expense of automated axial scanning, we demonstrate 3D DPC without a z-stage by hand spinning the microscope’s defocus knob to scan the object axially while updating illumination patterns on the LED-array microscope. We utilize an inverse problem optimization to retrieve the sample’s volumetric information with measurements from unknown axial positions by jointly solving for each measurement’s axial position. Finally, we explore how to optimize the LED-array illumination patterns for varying axial sampling rates.

Spatial-filtering-based quantitative phase imaging of transparent objects is demonstrated. By combining a standard phase contrast microscopy technique with an optical-system-mimicking Gerchberg-Saxton phase retrieval algorithm, we successfully transformed a qualitative phase map into an accurate quantitative phase distribution of the examined object. Feeding a phase-induced intensity image into the modified phase retrieval algorithm is found to facilitate the convergence and ambiguities issues associated with the standard algorithms. Accurate phase recovery for two different kinds of objects supports the use of the suggested method as a single-shot quantitative phase contrast microscope.

Each image metric represents different characteristics of images. For instance, similarity metrics, e.g. Structural Similarity Index Metric (SSIM) or Pearson Correlation Coefficient (PCC), utilize correlation between two images to calculate similarity between them; error metrics, e.g. Mean Absolute Error (MAE) or Root-Mean Squared Error (RMSE), compute the pixel-wise error between them according to different norms. As each of them highlights different aspects, a choice of the metric for an application may depend upon characteristics of the images. In this paper, we will show some tomographic reconstructions of dense-layered binary-phase objects, and as the objects are binary, we propose Probability of Error (PE) as an image metric for the assessment of the reconstructions in contrast to other metrics that are not constrained to the range of values. PE is equivalent to Bit-Error Rate (BER) in digital communications as both of the signals of interest are binary, and we are interested to a bit-wise deviation of the reconstructions of their corresponding ground truth images.

Accurate reconstruction methods are needed to extend the reach of quantitative 3D microscopy to diverse samples in biology with illumination in an arbitrary states of spatial and temporal coherence. Recent research in optical diffraction tomography (ODT) reconstruction has incorporated non-invertible physics-based forward models, such as beam propagation or the Lippman-Schwinger equation, to overcome these effects. However, partially coherent methods have yet to incorporate more accurate physical models, as these artifacts are reduced by the incoherence of the illumination and require heavy computation. Here we leverage the sparsity of the 3D Green’s function solution to the Helmholtz equation in k-space to reduce the problem’s dimensionality to rapidly compute a partially coherent forward model, allowing for a gradient descent-type iterative solver to reconstruct 3D thick biological samples in a variety of illuminations, including broadband light.

We present a physics-embedded deep learning architecture for improving the intensity diffraction tomography’s (IDT) 3D quantitate phase recovery of complex biological samples. Our approach utilizes both convolutional neural networks and IDT’s model-based volumetric reconstructions to provide accurate, physically-constrained predictions of multiple-scattering object volumes. We train and validate this learned IDT pipeline on simulated natural image datasets and show it generalizes well to recovering unseen complex biological specimens from experimental data.

Two features desired in 3D optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We validate SIMBA on intensity diffraction tomography (IDT). Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.

Structured illumination (SI) phase imaging is an important strategy to achieve quantitative phase imaging via encoding phase-induced diffraction into modulation intensity signals through propagation. However, the nonlinear property of SI-based transfer function results in ill-posedness in phase imaging retrieval. Overlapping modulation spectrum usually leads to loss of high spatial frequency components. Recent studies show that such nonlinear inversion problems can be efficiently represented by deep neural networks, as have been demonstrated in phase retrieval via holography and Fourier ptychography techniques. Here we present a hierarchical synthesis network (HSNet) which uses multiple splitting networks to extract structural features of structured intensity images in various modulation frequency and synthesis network to produce high fidelity reconstruction. We show that the proposed framework retrieve clear and accurate phase profile with reduced computing requirements in simulation.

Phase retrieval methods have been proposed to recover phase from through-focus intensity images measured on a microscope. It requires measuring the defocus distances of intensity images, usually by a z-axis translation stage. However, it complicates the experiment, adds expense, and suffers from error in the z-stage measurement. In this paper, we propose an algorithm to recover phase from only through-focus intensity images without measuring extra parameters such as defocus, wavelength and pixel size. We formulate the phase retrieval as a least-square error optimization problem and propose an alternating update scheme to solve the nonconvex optimization problem. We validate the algorithm with simulation and experiment.

In this work, we show that deep learning and SLIM can be combined to quickly deliver superior results in tissue screening applications. This concept of combining QPI label-free data with AI with the purpose of extracting molecular specificity has been recently introduced by our laboratory as phase imaging with computational specificity (PICS) [Nat. Comm., in press]. Training on ten thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate the gestational size: either appropriate for gestational age (AGA) or small for gestational age (SGA), and diet: either an experimental regimen high in hydrolyzed fats or a control diet, with an accuracy of 80% and 81%, respectively, and a four-parameter classification (diet and size) with 62% accuracy. These results are significant, as it would otherwise be impossible for a trained histopathologist to distinguish such discrepancies.

In this study, we used spatial light interference microscopy (SLIM), an ultrasensitive QPI method, and deep learning, to first generate a virtually-stained micrograph of a blood smear. This approach of combining label-free QPI data with deep learning to infer chemical specificity has been recently developed in our laboratory and is referred to as PICS [Nat. Comm., in press]. Next, we applied a computational semantic segmentation to identify and delineate the white blood cells. Lastly, we ran a classification model on the leukocytes to identify their type and condition. PICS renders synthetically stained blood smears rapidly, at a reduced cost of sample preparation, and provides quantitative clinical information. We validated this approach by successfully creating computationally stained micrographs and classified the leukocytes into five cell classes, with 92% accuracy.

In this study, we use Spatial light interference microscopy (SLIM), a quantitative, label-free imaging technique, to study E. coli, phages, and their interaction. The first part of the study involves imaging of infected, fluorescently-labelled E. coli cells. Bacterial genome and prophage were labelled with fluorescent proteins. The SLIM images overlay very well with the fluorescence images, proving that SLIM can detect subcellular features of E. coli. The second part of the study involves imaging isolated phages. Again, using fluorescence as ground truth, our results show that SLIM is able to detect and locate individual bacteriophages.

In this study, we use phase imaging with computational specificity (PICS) to detect single Adenovirus and SARS-CoV2 particles. These viruses are sub-diffraction particles, with maximum diameter of approximately 120nm, which implies that we cannot fully visualize their internal structure. However, due to the very high spatial sensitivity of SLIM (0.3 nm pathlength), we can detect and localize individual viruses and, furthermore, using deep learning, classify them with high accuracy.

Although oxidative stress is believed to play an important role in numerous diseases, current methods of measuring the oxidative stress level do not provide a consensus on how to quantify and compare the level of oxidative stress. Here, we suggest optical diffraction tomography (ODT) as a label-free cellular oxidative stress level quantification method. ODT measurements of HS68 cells treated with hydrogen peroxide solution showed that the oxidative stress level can be quantified based on the biophysical features from the refractive index (RI) tomogram. Quantifying the cellular oxidative stress based on the RI measurements will provide a direct guideline for the level of cell stresses on both academic and clinical situations.

Biological analysis of lipid droplets (LDs) in foam cells, a characteristic feature of atherosclerosis, along with the screening of drugs to inhibit or ameliorate this phenomenon has been limited by the lack of live cell imaging modalities and the requirement of chemical labels and fixatives. Here we utilize 3-D optical diffraction tomography (ODT) integrated with fluorescence imaging to obtain a specific refractive index of LDs along with their quantitative morphological and biophysical parameters in label-free living cells. In addition, as a proof-of-concept, we demonstrated the therapeutic efficacy of a nanodrug designed to effect the targeted delivery using time-lapse monitoring of live cells. These data support the potential utility of the developed label-free 3-D ODT imaging technique as a promising tool for LD characterization and to screen and track the efficacy of novel drugs even in the single-cell level against degenerative diseases.

Cell size and growth are tightly regulated processes balancing synthesis and metabolism to ensure proper cell function. Deviations from normal growth in response to drug treatment provide insights into the induced cellular dysfunction caused by pharmacotherapy. These changes in biomass growth have shown promise as a marker for drug sensitivity. However, like many biomarkers, the output cannot be treated as binary. Just as cancer cells are heterogenous on the molecular level, individual cells’ biomass response to drug treatment can range from cell death to no effect. It is therefore important to begin to survey for the full range of biomass growth responses to drug treatment to understand the dysfunction induced. Here, we explore the response of different cancer cell lines to treatments that induce biomass growth changes ranging from apoptosis to senescence to simply delayed regrowth. These longer-term studies, ranging up to six days of constant monitoring, aid in the interpretation of more commonly performed shorter-term biomass growth experiments and identify cells of interest for further molecular characterization in these cell lines.

Malignant transformation of normal epithelial cells involves extensive epigenetic reprogramming that alters the structure of chromatin within the cell nuclei. We have shown that Fourier phase associated with nanoscale nuclear architecture mapping (nanoNAM) of epithelial cells based on Fourier-domain optical coherence tomography (FD-OCT) can capture the resulting changes in nuclear architecture via nanoscale-sensitive joint characterization of coherence-gated mean alteration in nuclear refractive index and its associated heterogeneity. Here, using computational modeling and stochastic optical reconstruction microscopy (STORM) based super-resolution imaging, we demonstrate the underlying structural changes of chromatin remodeling during malignant transformation in epithelial cells measured by depth-resolved nanoNAM.

Interaction between intracellular compartments is based on complex vesicular transport processes. Fluorescence based techniques now reach single molecule resolution in living cells. To avoid fluorophore photobleaching limitations, quantitative phase imaging techniques are conventionally used to produce long-term high contrast cell images. We used an improved wave front sensing set-up called QWLSI-HD, which resolution is high enough to reach nanometric resolution for vesicle localization. We also added specificity to quantitative phase imaging by training artificial intelligence algorithms to recognize intracellular vesicles. We compared performances between the algorithms and also between fixed and living samples.

We demonstrate that live-dead cell assay can be conducted in a label-free manner using quantitative phase imaging and deep learning. We apply the concept of our newly-developed phase imaging with computational specificity (PICS) to digitally stain for the live/dead markers. HeLa cultured mixed with viability fluorescent reagents (ReadyProbes, ThermoFisher) were imaged for 24 hours by spatial light interference microscopy (SLIM) and fluorescent microscopy. Based on the ratio of the two fluorescence signals, semantic segmentation maps were generated to label the state of the cell as either live, injured, or dead. We trained an EfficientNet to infer cell viability from SLIM images with semantic maps as ground truth. Validated on the testing dataset, the trained network reported an F1 score of 73.4%, 97.0%, and 94.3% in identifying live, injured, and dead cells, respectively.

Transport of Intensity Equation (TIE) is a powerful computational tool for quantitative phase imaging using intensity only measurement. However, one drawback of TIE is that it does not include any parameters of the imaging system in the equation. To account for the effect of the imaging system on the retrieved phase, TIE is reformulated using Contrast Transfer Function (CTF) to analytically derive the distortion functions present in TIE. The distortion function attenuates the frequency components in the pass band resulting in a blurry phase image. For image restoration, signal parameters are estimated by minimizing a cost function for power spectrum and an optimal wiener filter is employed to deconvolve the distortion function. The proposed method is experimentally demonstrated through a visible enhancement in the phase images of human cheek cells obtained using a bright field microscope.

In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval problem in a lensless optical system from a single observation. We utilize U-net structured DCNN to reconstruct phase from the amplitude images at the sensor plane, and after applying computational backpropagation, complex objects' amplitude is reconstructed at the object plane. Results are demonstrated by simulation experiments.

We demonstrate a fiber-based quantitative phase imaging (QPI) system with epi-illumination to acquire tissue and cellar level structure. Our approach is based on quantitative oblique back-illumination microscopy (qOBM), which keeps the advantages of QPI—label-free and non-destructive with nanometer-scale sensitivity—while also delivering tomographic sectioning capabilities in thick scattering samples using epi-illumination. The developed system uses a simple and robust configuration consisting of a flexible fiber bundle and a GRIN lens. Here data are presented with histopathological feature analysis. This technique, with its compact setup and real-time processing algorithm, can lead to in-vivo medical diagnosis, for clinical surgery and endoscopy.

Recent advances in optical diffraction tomography (ODT) have enabled the observation of subcellular structures in living cells through mapping their three-dimensional (3D) refractive index distributions. Illumination angle-scanning based QDT can achieve around 200 nm lateral imaging resolution, however, microscopic structures smaller than 500 nm may not even be clearly observed in living cells. Therefore, we explore the factors that can influence the imaging contrast, including the number of angles used for reconstruction, sample refractive index contrast, and the size of sample features. This study will provide a guidance on optimizing ODT for achieving high contrast cell imaging.

Quantitative Phase Imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method. However, as a phase-shifting technique, SLIM is limited in acquisition rate to at most 15 fps. On the other hand, Diffraction Phase Microscopy (DPM) is such a method, with the advantage of being common-path. However, laser-based DPM systems are plagued by spatial noise due to speckles and multiple reflections. Here, we propose using deep learning to produce SLIM-quality phase maps from DPM, single shot, images. We constructed a deep learning model based on U-Net and trained on over 1,000 pairs of DPM and SLIM images. From the test set, we observed that the model learned to remove the speckles in DPM and overcame the background phase noise. We implemented the neural network inference into the live acquisition software and allows us to acquire single-shot DPM images and infer from them SLIM images in real time.

We demonstrate a new portable multi-wavelength fiber-based quantitative phase microscope (FQPM). In FQPM, the reference beam is made to propagate in a single-mode optical fiber whose length is tuned to ensure the best interference contrast with the sample beam. For further compactness, different illumination wavelengths are multiplexing through fiber combiners and couplers. FQPM simultaneously acquires the sample interferograms coming from two illumination wavelengths in a single shot, and their corresponding phase maps can be obtained separately. We have used FQPM for imaging dispersive samples, such as fluorescence particles and red blood cells and further analyze the refractive index and intracellular hemoglobin concentration.

Transport of Intensity equation(TIE) is a non-interferometric method used for quantitative phase imaging. By reformulating the TIE using Contrast Transfer Function, it can be determined that the spatial resolution of the phase retrieved using TIE is limited by the product of imaging system transfer function and a sinc function. In this work, we apply the principles of structured illumination fluorescent microscopy to develop a TIE based super resolved phase imaging technique. The sinusoidal intensity pattern down modulates high frequency spectrum of the phase into the system pass band thereby providing a convenient approach to synthetically enlarge the numerical aperture of the system. Resolution enhancement by two folds is demonstrated using simulations.

Wireless communication can break the limitations of space and enable data transferring between disconnected equipment. Automatic and remote-controlled experimental equipment is required for factories or when it is inconvenient to work in the lab, e.g., during the pandemic. We implemented and demonstrated a wireless and automated quantitative phase microscope (QPM) system which can be used for observation of samples remotely or in a confined space. Microscopic manipulations, such as sample placement and scanning, can be operated through a robotic arm and motorized stages controlled by an embedded computing board equipped with a Wi-Fi connection.

Optical diffraction tomography (ODT) as a kind of label-free three-dimensional imaging technique has been widely used in biomedical imaging. To decrease the volume of ODT instrumentation and broaden the applications of the ODT technique, we propose a novel design to achieve a miniaturized ODT system based on using a vertical-cavity surface-emitting laser (VCSEL) array and a self-interference unit. In our proposed system design, a dome-shaped VCSEL array is used to generate angle scanning illuminations, and the self-interference unit is applied to record the off-axis interferograms from different illumination angles. We have verified the accuracy of phase retrieval of our system and investigated the proper illumination angles and reconstruction model for further system development.

We propose a new label-free widefield super-resolution imaging technique which involves structured illumination and second harmonic generation (SHG). In an interferometric setup with a reference beam and a sample beam, the use of structured light effectively doubles the region of detectable spatial frequencies. This procedure utilizes both, intensity and phase information of the sample. By solving the wave equations for SHG fields, the images can then be reconstructed in Fourier space with a spatial resolution up to four times the Abbe diffraction limit.

Quantitative phase imaging (QPI) has been demonstrated to be a versatile tool for minimally invasive label-free imaging of biological specimens and time-resolved cellular analysis. In cytotoxicity testing, a commonly performed task in the preclinical development of new medical products, a flexible time-resolved label-free quantification of drug mediated effects is highly desirable. We thus explored the capabilities of digital holographic microscopy (DHM), an interferometric variant of QPI, to determine the temporal dry mass development of a cell population of RAW 264.7 mouse macrophages to quantify effects caused by medical nanoparticles in vitro in comparison to digitonin, which was used as cytotoxic positive control, and untreated cells in culture medium. Digital off-axis holograms from medical nanoparticles and digitonin incubated cells as well as from untreated control cells were acquired automated in multi-well imaging plates using a modular DHM system that was equipped with a motorized stage. From the retrieved time-lapse series of quantitative phase images, the temporal development of the dry mass in the observed field of view was calculated. As dry mass is correlated to cellular proliferation, toxicological effects of the medical nanoparticles on macrophages can be quantified at the nanogram scale. We found a similar temporal dry mass development of control macrophages in the cell culture medium and after incubation with non-toxic liposomes, while cytotoxic poly(alkyl cyanoacrylate) nanoparticles significantly reduced the dry mass increment in a dose-dependent manner. In summary, our results show for macrophages that the observation of the temporal dry mass development allows to discriminate reliably between non(low)-toxic and harmful nanomaterials.

Quantitative phase imaging (QPI), with its capability to capture intrinsic contrast within transparent samples, has emerged as an important imaging method for biomedical research. However, due to its label-free nature, QPI lacks specificity and thus faces limitations in complex cellular systems. In our previous works, we have proposed phase imaging with computational specificity (PICS), a novel AI-enhanced imaging approach that advances QPI by utilizing deep learning for specificity. Here we present that PICS can be applied to study individual cell behavior and cellular dry mass change across different phases of the cell cycle. The cell cycle information is traditionally obtained by fluorescence microscopy with markers like Fluorescence Ubiquitin Cell Cycle Indicator (FUCCI). Our work showed that using deep learning, we can train a neural network to accurately predict the cell cycle phase (G1, S, or G2) for each individual cell.

Three-dimensional (3D), label-free, and long-term imaging in organ-on-a-chip research is necessary to assess the dynamic development of multicellular structures inside a microfluidic chip and relevant physiological functions. However, imaging on-chip multicellular landscapes generally relies on the use of exogenous labels, which limits the long-term imaging of live cells due to photobleaching and phototoxicity. Here, we demonstrate the 3D label-free observation of 5-day on-chip vasculogenesis of human umbilical vein endothelial cells using optical diffraction tomography. The proposed method enables us to quantify both morphological and biochemical alterations during the vasculogenesis.

Observation of living plant cells under conventional brightfield microscopy suffers from low imaging contrast. Therefore, fluorescence labeling or fluorescence tag is typically required, but it may not reflect the bona fide state of cellular events and prevent long-term observation due to photobleaching of the fluorescence signal. Therefore, we propose to use quantitative phase imaging (QPI) for label-free imaging of plant cell structures. Using QPI, we have observed vacuoles and nucleus in tobacco BY-2 cells, Arabidopsis cell suspension culture PSB-D, and pollen tubes.

Biopolymers are actively used in tissue engineering for the production of biomedical products with desired and controlled properties. Particular attention is paid to the creation of an artificial biomimetic matrix - a framework for targeted regeneration, effective cell growth and their integration with the surrounding tissue. The purpose of the work is to identify the possibilities of interference microscopy for studying the structural, functional and biological properties of biopolymer matrices based on fibroin and chitosan. The analysis of the thickness, relief features, and structure of biopolymer matrices was carried out using methods of superresolving interference and atomic force microscopy. We used a MIM-340 laser modulation interference microscope (Shvabe, Russia) with following technical characteristics: resolution in the lateral plane - 13 nm; vertical resolution - 0.1 nm; the range of measurement of linear vertical dimensions - 0.01–0.10 μm, in the lateral plane (for a 20x lens) - 0.1–8.0 μm; field of view of the measuring channel in the lateral plane of not less than 20 microns. Atomic force microscopy was performed on the basis of the NtegraPrima micro-cantilever system (NT-MDT, Russia) in semi-contact mode. We visualized and quantified the relief and heterogeneity of the internal structure of biopolymer matrices in the obtained interferograms. The effect of the addition of fibroin and jenipine on the thickness, roughness, and structural uniformity of the composite matrix coating has been studied. To assess the biocompatibility, adhesive and immunogenic properties of chitosan and fibroin-containing polymers, populations of living cells (lymphocytes, neutrophils and mesenchymal stem cells) were used. High informativeness of the QPI in the study of the morpho-functional properties of films and nanofiber materials was established. Based on the results of a comparative analysis of new biopolymer composites, the most promising samples for creating medical devices with characteristics as close to physiological as possible are determined.

In this work, the Bessel beam from the optical fiber negative axicon probe is utilized for spectral domain optical coherence phase microscopy (SDOCPM) based on common-path interferometry. An optical fiber negative axicon probe generating the Bessel beam is fabricated by the chemical etching at the distal end of a high germanium doped optical fiber in hydrofluoric acid (48% HF) under the effect of capillary action. This probe produces a non-diffracting Bessel beam having a long depth of focus (DOF), ~700μm and very small spot size. This is basically a lens-less approach for SDOCPM. The wrapped phase is retrieved in the spectral domain, afterwards, the unwrapping is done. The absolute optical path length (OPL) is estimated by the slope of the unwrapped phase which is further fitted by a least-square algorithm. The retrieved unwrapped phase is assumed as a reference to correct the wrapped phase obtained by the conventional direct phase extraction method. The phase fluctuation is evaluated to be ≤ 0.6 milliradian by a stationary coverslip of ~150μm and the corresponding displacement sensitivity in the free space is ~10 pm. The axial resolution and lateral resolution of our system in the air are found to be ~6.9μm. We successfully demonstrate the potential of our system for biological application by imaging the onion skin as a sample.