Wedge prism assisted quantitative phase imaging on standard microscopes

Differential phase contrast (DPC) is a non-interferometric quantitative phase imaging method achieved by using an asymmetric imaging procedure. We report a pupil modulation differential phase contrast (PMDPC) imaging method by filtering a sample's Fourier domain with half-circle pupils. A phase gradient image is captured with each half-circle pupil, and a quantitative high resolution phase image is obtained after a deconvolution process with a minimum of two phase gradient images. Here, we introduce PMDPC quantitative phase image reconstruction algorithm and realize it experimentally in a 4f system with an SLM placed at the pupil plane. In our current experimental setup with the numerical aperture of 0.36, we obtain a quantitative phase image with a resolution of 1.73μm after computationally removing system aberrations and refocusing. We also extend the depth of field digitally by 20 times to ±50μm with a resolution of 1.76μm.

Hybrid Random Walk-Linear Discriminant Analysis Method for Unwrapping Quantitative Phase Images of Biological Samples

Dielectric metasurfaces have recently attracted much attention due to their ability to efficiently control light propagation, that is, its amplitude, phase and polarization, on micro- and macro- levels. Such metasurfaces also naturally allow for integration in compact optical setups due to their small sizes. An important issue of energy losses is also addressed by employing all-dielectric platform. Advancements in nanofabrication have enabled realization of metasurface-based high-resolution wavefront engineering devices, which have been employed in different imaging applications, for example, as metalenses, polarization filters, metaholograms, etc. Quantitative phase imaging is a powerful tool for the optical inspection of transparent samples in connection with biological and technical applications. By measuring thickness and/or refractive index profiles, phase imaging facilitates, for example, the observation of dynamic events in unstained cells. As such, it has become indispensable in biological imaging, wavefront correction and metrology. However, conventional cameras and photodetectors are inherently not sensitive to the phase of an incident lightwave. As a consequence, direct phase detection is challenging. Only by multiple intensity measurements or application of specialized interferometric schemes, the phase profile of an object can be in principle reconstructed. In this paper, we report on the ability of metasurfaces to contribute to quantitative phase imaging by independent control of the phase profiles in two orthogonal polarization states of an incident beam.

It is well known that the quantitative phase information which is vital in the biomedical study is hard to be directly obtained with bright-field microscopy under incoherent illumination. In addition, it is impossible to maintain the living sample in focus over long-term observation. Therefore, both the autofocusing and quantitative phase imaging techniques have to be solved in microscopy simultaneously. Here, we propose a lightweight deep learning-based framework, which is constructed by residual structure and is constrained by a novel loss function model, to realize both autofocusing and quantitative phase imaging. It outputs the corresponding in-focus amplitude and phase information at high speed (10fps) from a single-shot out-of-focus bright-field image. The training data were captured with a designed system under a hybrid incoherent and coherent illumination system. The experimental results verify that the focused and quantitative phase images of non-biological samples and biological samples can be reconstructed by using the framework. It provides a versatile quantitative technique for continuous monitoring of living cells in long-term and label-free imaging by using a traditional incoherent illumination microscopy system.

Inflammatory bowel diseases (IBD) are inflammatory disorders of the gastrointestinal tract characterized by a chronic relapsing disease course. As uncontrolled intestinal inflammation can result in severe disease complications, recent treatment targets of IBD evolved toward seeking the absence of mucosal and histological inflammation. However, this approach requires adequate histological evaluation of IBD disease activity. The diagnostic challenge of histological examination of intestinal inflammation is documented by the multitude of proposed histological scoring systems. In this context, we review quantitative phase imaging (QPI) techniques such as digital holographic microscopy (DHM) for characterizing intestinal inflammation. DHM determines optical path-length delays in a stain-free manner, thereby providing the tissue refractive index as a biophysical marker that directly correlates to tissue density. Recently, DHM has been successfully applied in cell biology, cancer cell research and infectious-induced cellular alterations. We summarized the capabilities of DHM and related QPI techniques to assess the severity of intestinal inflammation in experimental colitis as well as in colonic samples from human IBD patients. Moreover, we illustrate major advantages of DHM facilitated multimodal evaluation of epithelial wound healing processes as assessed by physical parameters like cell volume, density, thickness and dry mass in vitro. Furthermore, potential limitations of DHM and future utilities of QPI are discussed. In conclusion, DHM represents a promising, easy-to-use quantitative tool to provide accurate and objective assessment of intestinal inflammation and may pave the way towards automated label-free digital pathology and related in vitro cell culture analysis in future.

Measuring the average refractive index (RI) of spherical objects, such as suspended cells, in quantitative phase imaging (QPI) requires a decoupling of RI and size from the QPI data. This has been commonly achieved by determining the object's radius with geometrical approaches, neglecting light-scattering. Here, we present a novel QPI fitting algorithm that reliably uncouples the RI using Mie theory and a semi-analytical, corrected Rytov approach. We assess the range of validity of this algorithm in silico and experimentally investigate various objects (oil and protein droplets, microgel beads, cells) and noise conditions. In addition, we provide important practical cues for the analysis of spherical objects in QPI.

Motility, or co-ordinated movement, of cells is fundamental for the development of many processes, such as formation of the embryo, the immune response, inflammation, wound healing, and metastasis of cancer cells 1. Cells often migrate in response to specific external signals, but they can also move spontaneously. Aberrant cell motility can be fatal in cancer diseases when cells start to metastasize 2. Metastasis is strictly associated with tissue invasion of the primary tumor cells, and occurs as a multistep progress 3. The first phase of cancer cell metastasis involves the physical translocation of the cell to a distant organ, whereas the second phase implicates the ability of the cancer cell to develop into a metastatic lesion at that distant site 4. For skin cancer melanoma, the main cause of death is widespread metastases 5. Melanoma cells may travel along external vessel lattices. After settling in the metastatic sites, melanoma cells develop mechanisms that protect them against the attack of the immune system. It is, therefore, of great interest and importance to investigate, monitor, and understand the principles of tumor cell invasion and migration. The development of novel therapeutic applications that have the ability to block migratory processes is crucial. For in vitro studies, the cell culture wound-closure, and the transwell migration and invasion assays can provide data for an understanding how well a particular cell type can migrate 6. The wound healing assay is also called a scratch assay because it is performed by making a scratch in the cell monolayer (Fig. 1A). The transwell migration assay is a commonly used test to study the migratory response of endothelial cells to angiogenic inducers or inhibitors (Fig. 1B). This assay is also known as the Boyden or modified Boyden chamber assay. These assays have been frequently used to study movement of cells. Weckmann et al. developed an interesting system to monitor and quantify neutrophils on single cell level, by utilizing commercially available tissue culture hardware, simple video microscopic equipment, and standardized tracking 7. However, the interest for novel standardized real-time analyses using quantitative phase imaging (QPI) for cell tracking is growing (Fig. 2). More than 10 years ago, the digital holographic (DH) microscopy methodology of QPI became known to a larger audience and the technique has since then successfully been applied for label-free monitoring of live cells undergoing cell division, cell proliferation, cell death, and cell migration over time 8-10. By QPI, studies associated with both cellular thickness and refractive index fluctuations have been performed. The possibilities for utilizing QPI for cancer diagnostics have increased 11. Cellular behavior and phenotypes are other parameters that can be measured 12. For two human osteosarcoma cell types, the motility pattern, but not the proliferative pattern, was different. Early migration studies using DH microscopy showed fibroblast migration 13 and HT-1080 fibrosarcoma cells embedded in a 3D collagen gel 14. Alm et al. and Persson et al. also presented data on movement of different cell types and cell sizes 15 as well as demonstrated the correlation of cell movement with cell morphology 16. In the recent article published in the November 2018 issue of Cytometry Part A by Zhang et al., (pages 1125–1131) commercially available DH cytometry platforms equipped with semi-automated image acquisition, segmentation, and analysis software packages for assessing cell behavior are used and discussed. The motility data was obtained with QPI/cytometry using "Track cells module" and "Wound healing module," respectively, and with transwell migration assays determining migration and invasion, respectively. The different techniques assessing motility were compared. The two melanoma cell lines, WM793 and 1205Lu, used in the study were reported to have different metastatic capacity, resulting in differences in proliferation and motility. The motility measured with the "Track cells module" and "Wound healing module," using DH cytometry equipment housed in an incubator, produced highly correlated relative motilities, even when compared to the standard transwell migration and invasion assays. Images acquired with DH are from the same position in a cell culture vessel, making it possible to study individual cells. This is a strong advantage, since most studies of effects of new drugs on proliferation and migration of cancer cells in culture are performed on a population level. In a recent study by Kamlund et al., cell images were captured every fifth minute for a total of up to 72 h 17. For cell movement studies, the semi-automated algorithm attempts to find each tracked cell in the next frame by selecting the closest cell based on the centroid position. The advantages of the Track cells and Wound healing modules over the transwell migration and invasion assays in the paper by Zhang et al., included the possibility to use the cells in the experiment for other purposes after completing the measurement. DH microscopy also benefits from the fact that morphology analysis can be performed of each cell. Indeed, this opens up for almost unlimited possibilities to perform cell morphology analysis using the QPI methodology, since each image is very rich in cellular information. As discussed in a review by El-Schich et al., automated analysis and machine learning is one approach to handle the large amount of morphological data that can now be generated when analyzing cells with QPI 8. Only the computer resources and the training speed will set the limitations.

Intestinal strictures are a frequent complication in patients with Crohn’s Disease (CD) and the presence of fibrosis within strictures impacts the therapeutic treatment approach. Here, we evaluate quantitative phase imaging (QPI) using digital holographic microscopy (DHM) for the evaluation of fibrosis within CD strictures. 30 full thickness resection specimens were obtained from non-stenotic and stenotic tissue areas of 15 CD patients. Cryostat sections were analyzed by DHM to measure the spatial distribution of the refractive index (RI) to quantify tissue density. Complementary, histopathological evaluation of H&E staining and immunofluorescence (IF) targeting fibrosis markers served as the gold standard. Moreover, tissue stiffness was evaluated by elastography. RI values assessed by DHM were significantly higher in stenotic compared to non-stenotic tissue areas (p < 0.001). Histopathological analysis using H&E staining and IF confirmed the elevated expression of fibrosis markers in stenotic compared to non-stenotic tissue (all p < 0.001). The RI retrieved by DHM strongly correlated with the amount of fibrosis as determined by IF (p < 0.001; R2 = 0.48). Furthermore, elastography detected a significantly higher tissue stiffness in stenotic as compared to non-stenotic tissue sections (p < 0.001). In conclusion, QPI using DHM accurately assesses fibrotic properties of CD-associated strictures and may improve the characterization of CD strictures.

Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.

Quantitative phase imaging (QPI) has advanced by accurately quantifying phase shifts caused by weakly absorbing biological and artificial structures. Despite extensive research, the diffraction limits of QPI have not been established and examined. Hence, it remains unclear whether diffraction-affected QPI provides reliable quantification or merely visualizes phase objects, similar to phase contrast methods. Here, we develop a general diffraction phase imaging theory and show that it is intrinsically connected with Rayleigh’s resolution theory. Our approach reveals the entanglement of phases under restoration, imposing diffraction bounds on spatial phase resolution and, unexpectedly, on phase accuracy. We prove that the phase accuracy depends on the size, shape, and absorption of objects forming the sample and significantly declines if the object size approaches the Rayleigh limit (a relative phase error of −16% for an Airy disk-sized object with low phase shift). We show that the phase accuracy limits can be enhanced at the cost of deteriorated phase resolution by attenuating the sample background light. The QPI diffraction limits are thoroughly examined in experiments with certified phase targets and biological cells. The study’s relevance is underscored by results showing that the phase accuracy of some structures is lost (a relative phase error of −40%) even though they are spatially resolved (a phase visibility of 0.5). A reliable procedure is used to estimate phase errors in given experimental conditions, opening the way to mitigate errors’ impact through data post-processing. Finally, the phase accuracy enhancement in super-resolution QPI is discovered, which has not been previously reported.

Fourier Ptychographic Microscopy (FPM) and Differential Phase Contrast (DPC) are quantitative phase imaging (QPI) methods that recover the complex transmittance function of a sample through coded illumination measurements and phase retrieval optimization. The successes of these methods rely upon acquiring several or possibly hundreds of illumination-encoded measurements. The multi-shot nature of such methods limits their temporal resolution. Similar to motion-induced blur during a long photographic exposure, motion occurring during these acquisitions causes spatial distortion and errors in the reconstructed phase, which inhibits these methods' ability to image fast moving live samples. Here we present a novel approach to correct for motion during QPI capture that relies on motion navigation to register measurements together prior to phase retrieval. The different illumination patterns required for QPI cause the measurements to have a different contrasts. This makes it difficult to use standard registration approaches to estimate complex sample motion directly from the measurements. Instead, we use a color-multiplexed navigator signal (red) that is comprised of a constant illumination pattern and leverage a color camera to separate it from the primary QPI information (green). The reliable motion estimate allows measurements to be shared across time points through image registration. This enables a full set of measurements for a phase retrieval problem to be solved at each time point. We demonstrate proof-of-concept experimental results in which blurring due to live sample motion (swimming Zebra fish, cell motion, and organelle movement) is reduced.

Coherent imaging enables noninvasive, label‐free, and quantitative monitoring of the dynamic motions of transparent microobjects requested in life sciences, biochemistry, material sciences, and fluid mechanics. Quantitative phase imaging (QPI), a coherent imaging technique, provides full‐field optical phase information through light interference. The use of coherence, however, inevitably accompanies phase ambiguity and coherent artifacts, such as speckle, diffraction, and parasitic interference, which severely deteriorate the interferograms to hinder successful phase reconstruction. Herein, it is demonstrated that a frequency comb can newly provide a wide coherence tunability for higher visibility interferograms, phase‐coherent multiple wavelengths for extracting physical height information from refractive index, and higher phase stability (2.39 × 10−3 at 10 s averaging time) at a higher speed up to 16.9 kHz. These superior characteristics of frequency‐comb‐referenced QPI will enable in‐depth understanding of dynamic motions in cellular, biomolecular, and microphysical samples.

We explored a Mask Region-based Convolutional Neural Network (Mask R-CNN) to detect macrophages in quantitative phase images, which were acquired by digital holographic microscopy (DHM), an interferometry-based variant of quantitative phase imaging (QPI). The Mask R-CNN deep learning architecture is capable to detect and segment single macrophage cells in quantitative phase images and allows to perform both tasks in a multi-stage process. Our results show that the combined detection and segmentation of cells through Mask R-CNN-based automated evaluation prospects a fast and robust screening in label-free high throughput microscopy.

Quantitative phase measurements at multiple wavelengths has created an opportunity for exploring new avenues in phase microscopy such as enhancing imaging-depth (1), measuring hemoglobin concentrations in erythrocytes (2), and more recently in tomographic mapping of the refractive index of live cells (3). To this end, quantitative phase imaging has been demonstrated both at few selected spectral points as well as with high spectral resolution (4,5). However, most of these developed techniques compromise imaging speed, field of view, or the spectral resolution to perform interferometric measurements at multiple colors. In the specific application of quantitative phase in studying blood diseases and red blood cells, current techniques lack the required sensitivity to quantify biological properties of interest at individual cell level. Recently, we have set out to develop a stable quantitative interferometric microscope allowing for measurements of such properties for red cells without compromising field of view or speed of the measurements. The feasibility of the approach will be initially demonstrated in measuring dispersion curves of known solutions, followed by measuring biological properties of red cells in sickle cell anemia.

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