This PDF file contains the front matter associated with SPIE Proceedings Volume 11970, including the Title Page, Copyright information, and Table of Contents.

We reported a novel non-interferometric and non-iterative computational imaging method, synthetic aperture imaging based on Kramers-Kronig relations (KKSAI), to reconstruct complex wave-field. By collecting images through a modified microscope system with pupil modulation capability, we show that the phase and amplitude profile of the sample at pupil limited resolution can be extracted from as few as two intensity images by exploiting Kramers-Kronig relations. KKSAI reconstruction is non-iterative, free of parameter tuning and applicable to a wider range of samples. Simulation and experiment results have proved that it has much lower computational burden and achieves the best reconstruction quality when compared with two existing phase imaging methods.

We propose quantitative phase microscopy (QPM) with second-harmonic generation (SHG) and structured illumination, namely S²QPM, that can achieve fourfold resolution enhancement compared with normal incident illumination. The wave propagation and reconstruction model in S²QPM are provided from solving the inhomogeneous nonlinear Helmholtz equation in the k-space. Our derived physical model is accurate and does not involve far-field approximations. To validate our theoretical model, a Siemens star phase object has been reconstructed under simulated experimental conditions. We envision that S²QPM may potentially open more exciting applications in label-free bioimaging and material characterization. .

Real-time quantitative phase imaging is beneficial for observation and analysis of living cells. Despite off-axis interferometry-based quantitative phase microscopy (off-axis QPM) offers single-shot image acquisition, it usually requires a calibration image captured at a blank field of view to correct the aberration and a multi-step processing algorithm to reconstruct a phase map. Therefore, it is challenging to achieve real-time phase imaging. To simplify experimental operations and expedite image processing, we propose a lightweight U-Net based deep neural network for calibration-free and fast phase retrieval in off-axis QPM. Output phase maps of the lightweight U-Net achieve high fidelity with an average Structural SIMilarity (SSIM) index value of 90.2%. Via running this lightweight U-Net model on a laptop connected with a portable QPM system, we demonstrate an ease-of-use and compact QPM method that can be used for real-time imaging of living cells.

We introduce a lock-in method to increase the phase contrast in incoherent Differential Phase Contrast (DPC) imaging. The use of a smart pixel detector with in-pixel signal demodulation, paired with synchronized illumination, provides the basis of a bit-efficient approach to DPC. The experiments show an increased sensitivity by a factor of 8, for equivalent standard DPC measurements; single-shot sensitivity of 0.7 mrad at a frame rate of 1400 fps is demonstrated. This new approach may open the way for the use of incoherent phase microscopy in biological applications where extreme phase sensitivity and millisecond response time is required.

Quantitative phase microscopies (QPMs) have been mainly used for applications in cell biology, for around 2 decades. In this article, we show how cross-grating phase microscopy (CGM), a high-resolution, high-sensitivity QPM, recently expanded the scope of QPMs to applications in nanophotonics. In particular, this article explains how the intensity and phase images acquired by CGM can be processed to determine all the optical properties of imaged nanoparticles, 2D-materials and metasurfaces. We also explain how CGM can be used as a temperature microscopy technique. This latter imaging modality led to a large variety of works in the 2010s based on the optical heating of plasmonic nanoparticles for photothermal studies in physics, chemistry and biology at the microscale, in which label-free, microscale temperature measurements were pivotal.

Morphological changes in neurons are closely related to neurological disorders. Quantitative Phase Imaging (QPI) has been used to assess neuronal changes over time, using mass-sensitive contrast to quantitatively track network growth. QPI requires high quality segmentation of neurons in order to measure neuron cell body and neurite mass distributions. Neural networks are the state of the art for segmentation, but require thousands of images in order to generalize well. However, recent work on network functionality has shown that networks generalize by learning simple functions. Whether low data complexity hinders this has yet to be seen. Here we test this by simulating low complexity data, specifically, QPI images of neurons simulated using a neuronal growth model. We show segmentation results for feeding the network lab-acquired data.

Holographic tomography (HT) is a label-free, high-resolution and non-invasive method that retrieves 3D refractive index (RI) information about analysed biological specimens. The most common measurement scenario includes culturing and analysing cells directly in a Petri dish. However, it does not mimic the in vivo conditions unlike the microfluidic approach. Thus, in our work, we have focused on the development of a measurement configuration that is dedicated to analysis of cell dynamics in a lab-on-chip. It includes a fast HT system, a new ultra-thin microfluidic chip that allows for long term monitoring in controlled environment, a stitching algorithm that allows to combine single fields of view (FoV) into a synthetic field of view in three dimensions and the full volume RI analysis of internal cellular organelles during measurements. This setup provides the ability to track changes occurring in individual cell organelles as well as getting statistically valuable data. In experimental verification, A549-type and MeWo cells were cultured under microfluidic conditions in the chip and put under observation using HT.

High speed phase recovery from through-focus intensity images remains challenging. The limitation lies in the accumulated exposure time of intensity measurement. In this work, we propose to recover phase from the measurement of an event camera. Event cameras asynchronously measure intensity changes at microsecond resolution. It enables capturing the through-focus intensity changes with high speed. We build the forward model of the imaging process, which incorporates the temporal noise of event camera. We develop nonlinear optimization algorithms to solve phase from measured events. We validate the algorithms with simulations and test the effects of sensitivity parameters and temporal noise.

Deep learning techniques are always bound with big data and large, sophisticated models. In this paper, we show that this is not necessarily true for the task of end-to-end phase retrieval in off-axis interferometric quantitative phase imaging. For this task, we first introduce a new loss function, called bucket error rate (BER), for addressing the problem of imbalanced data distribution by balancing loss-bias of target and background area adaptively. With BER, we demonstrate that a U-Net model can learn the underneath logic for converting a raw interferogram to a phase map from only one training sample. At last, we present a novel mixed-context network (MCN) which can simultaneously aggregate local- and global-contextual information. Experimental results show that compared to U-Net, the proposed MCN is more accurate, more compact, and can be trained faster.

The imaging quality of in-line digital holography is challenged by the twin-image and aliasing effects because sensors only respond to intensity and pixels are of finite size. As a result, phase retrieval and pixel super-resolution techniques serve as the two essential ingredients for high-fidelity holographic imaging. In this work, we combine the two as a unified optimization problem, and propose a generalized algorithmic framework for pixel-super-resolved phase retrieval. In particular, we introduce the iterative projection algorithms and gradient descent algorithms for solving this problem. The basic building blocks, namely the projection operator and the Wirtinger gradient, are derived and analyzed. The algorithms are verified with both simulated and experimental data. The proposed framework generalizes well to various physical settings, and is compatible with many state-of-the-art optimization algorithms.

Holographic imaging plays an essential role in label-free microscopy techniques, and the retrieval of the phase information of a specimen is vital for image reconstruction in holography. Here, we demonstrate recurrent neural network (RNN) based holographic imaging methods that simultaneously perform autofocusing and holographic image reconstruction from multiple holograms captured at different sample-to-sensor distances. The acquired input holograms are individually back propagated to a common axial plane without any phase retrieval, and then fed into a trained RNN which successfully reveals phase-retrieved and auto-focused images of the unknown samples at its output. As an alternative design, we also employed a dilated convolution in our RNN design to demonstrate an end-to-end phase recovery and autofocusing framework without the need for an initial back-propagation step. The efficacy of these RNN-based hologram reconstruction methods was blindly demonstrated using human lung tissue sections and Papanicolaou (Pap) smears. These methods constitute the first demonstration of the use of RNNs for holographic imaging and phase recovery, and would find applications in label-free microscopy and sensing, among other fields.

We explored the capabilities of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) to quantify nanoparticle-induced tissue alterations and the performance of DHM QPI for imaging hematoxylin-eosin-stained samples. In a pilot study, paraffin and cryosections from nanomaterial-laden rat lungs and vehicle-treated control tissue were analyzed. Our results from rat lungs show that the tissue average refractive index and morphology parameters extracted from QPI images allow the quantification of visible tissue changes due to toxic nanomaterials. Moreover, our results demonstrate that DHM with near-infrared laser light enables high quality QPI of samples with common histology staining.

Angularly-resolved light scattering is useful for computing organelle size metrics of a cell due to its sensitivity to scatterer size and refractive index contrast. Unfortunately, the cell itself acts as a larger scatterer and contributes its own angular signature. For an adherent cell on a coverslip immersed in standard media with a refractive index close to that of water, we have found that the cell:media refractive index contrast can contribute significant scattering at angular deflections below twenty degrees. This whole-cell scattering, highly dependent on the cell’s shape and size, is challenging to distinguish from the desired organelle scattering signal. This degrades the accuracy with which organelle size information can be extracted from the angular scattering signal. To address the whole-cell contribution, we manipulate the refractive index of the immersion medium by mixing it with a water-soluble, biocompatible, high-refractive-index liquid. By minimizing the refractive index contrast between the cytoplasm and modified medium, this approach physically reduces the amount of whole-cell scattering. We demonstrate this technique on live cells, using a Fourier phase microscope to obtain the complex field of the sample and using Fourier transform light scattering to compute the angular scattering. Results show significant reduction of the whole-cell contribution, indicating the potential of this method for improving the estimates of organelle size distributions in single cells.

Single-shot phase-shifting digital holography (PSDH) implementations are attractive for quantitative phase imaging of dynamic objects, such as living cells; however, they require precise alignment of the optical elements. This study presents a novel single-shot PSDH configuration where the diffraction grating is placed on the focal plane of the object light path. The grating has a checkerboard pattern that provides spatially periodic phase shifts of 0 and π. The object light is diffracted by the grating such that the ±1st order diffractions form four wavefront copies. Since the spatial positions of these copies are determined using only the grating period, it is not necessary to precisely align the grating with the image sensor. The conventional PSDH involves phase shifts of the reference beam, whereas the proposed method allows advance phase shifts to occur between the object light copies. Therefore, phase-shifting interferograms can be obtained simultaneously by irradiating all copies with a uniform reference light. Since the phase-shift amounts between the copies depend on the lateral positions of the grating, it is necessary to estimate the degree of displacement of the grating to calculate the quantitative phase values. We therefore present a solution that adds two markers to the object light and estimates the grating displacements from the interference intensities between the markers and reference light. We also conducted numerical simulations to confirm that the proposed method obtains quantitative phase values from a single image.

Rapid assessment of the viability of E. coli and other bacteria pathogens is important for timely monitoring of water quality. Therefore, we propose a label-free method for assessing the viability of E. coli cells in a fast way by using quantitative phase microscopy (QPM) and machine learning. According to the viability levels, E. coli cell populations were divided into two classes that were treated with 0.9% and 25% sodium chloride (NaCl) suspended in phosphate-buffered saline (PBS) solution, respectively. Their high contrast phase images are acquired by a high sensitivity diffraction phase microscope. To determine the viability class of individual E. coli cells, a residual neural network (ResNet) is developed to extract the rich information contained in the phase images. An average testing accuracy as high as 95.5% has been achieved in predicting the two viability classes.

Significant improvements in quantitative phase imaging (QPI) technologies established digital holographic microscopy (DHM) and holotomography (HT) as tools for label-free imaging of cell morphology as well as to assess the 3D distribution of intracellular structures. Here, we applied DHM and HT consecutively on the same samples to observe and quantify the impact of nanomaterials on the morphology of primary hepatocytes. The liver has a high vascularization and metabolic activity, and hepatocytes were selected because they may contact and internalize nanoparticles circulating in the blood. Effects of nanoparticles on cells can range from a reduction of viability to alterations in morphology and intracellular structures. Thus, first an automated modular DHM setup was applied for large-area QPI screening of the entire hepatocyte populations while a commercial HT system (Tomocube HT-2H, Tomocube, Korea) was utilized to observe selected tiny 3-dimensional intracellular changes of interest via refractive index tomograms. For the investigations, hepatocytes were isolated from collagenase-perfused rat livers and seeded into HT-compatible cell culture dishes. After cultivation and incubation with different types of nanoparticles (CeO2, Ag, Au) for 24 hours the cells were fixed with a mixture of glutaraldehyde and paraformaldehyde to preserve cell morphology and structure. The results of our study demonstrate that QPI with DHM is capable for efficient large-area 2D screening and to reveal of nanomaterial-related alterations in the entire hepatocyte populations while HT provides high performance complementary 3D insights and the localization of tiny intracellular damages.

Quantitative phase microscopy (QPM) has been successfully applied to studying the biophysical properties of red blood cells (RBCs) in a label-free and high-throughput way. However, the lack of molecular specificity has hindered the applicability of QPM for further studies in RBCs. In this paper, we propose a compact and three-wavelength QPM method and demonstrate its potential for measuring molecular-specific properties of specimens. Using the quantitative phase images from three wavelength channels that are acquired in a single shot, we derive a model to obtain the oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) concentrations in RBCs. This new advance in QPM could be further applied to studying the morph-molecular properties of cells in real-time and characterizing cells in large populations to enable more frontier biomedical investigations.

We present single-shot quantitative phase imaging with polarization differential interference contrast (PDIC) for a slightly modified differential interference contrast microscope which records the unfiltered Stokes vector of the differential interference pattern with a polarization camera. PDIC enables single-shot high spatial resolution phase imaging in realtime, applicable to either absorptive or transparent samples, and integrates simply with epifluorescence imaging. As one application to neuroscience, we then demonstrate quantitative phase imaging of a whole mouse brain section by PDIC microscope with coregistered epifluorescence measurement of labeled oligodendrocyte progenitor cells (OPC) and oligodendrocytes (OL) cells.

We investigate the general adjustment of projection-based phase retrieval algorithms for use with saturated data. In the phase retrieval problem, model fidelity of experimental data containing a non-zero background level, fixed pattern noise, or overexposure, often presents a serious obstacle for standard algorithms. Recently, it was shown that overexposure can help to increase the signal-to-noise ratio in AI applications. We present our first results in exploring this direction in the phase retrieval problem, using as an example the Gerchberg-Saxton algorithm with simulated data. The proposed method can find application in microscopy, characterisation of precise optical instruments, and machine vision applications of Industry4.0.