Three-dimensional Quantitative Phase Imaging Research Articles

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Metrology for 3D Quantitative Phase Imaging Instrumentation

Three-dimensional quantitative phase imaging (3D QPI) delivers volumetric information about the refractive index (RI) distribution within microobjects and offers new solutions to outstanding challenges in cell biology and pathology [1-3]. Such imaging and measurement capabilities are of great interest to study the biophysical properties of biological microstructures such as single cells, cell aggregates, tissues or even small organisms, especially as they are complemented by high spatial resolution and label free sample preparation. Therefore, along with the development of multiplicity of research 3D QPI systems, the commercial devices using either spatial light interference (Phi Optics, Inc) or holographic tomography (TomoCube, Inc and NanoLive, Ltd) entered the market, readily tailored for microbiological applications. Despite of commercialization of these 3D QPI systems, their performance have not been fully quantified experimentally.

Three-Dimensional Quantitative Phase Imaging for Characterizing Lymphocyte Subtypes

Three-Dimensional Quantitative Phase Imaging for Characterizing Lymphocyte Subtypes

Three-Dimensional Quantitative Phase Imaging for Characterizing Lymphocyte Subtypes

Three-Dimensional Quantitative Phase Imaging for Characterizing Lymphocyte Subtypes

Analysis of structural effects of sickle cell disease on brain vasculature of mice using three-dimensional quantitative phase imaging.

Although the molecular origins of sickle cell disease (SCD) have been extensively studied, the effects of SCD on the vasculature-which can influence blood clotting mechanisms, pain crises, and strokes-are not well understood. Improving this understanding can yield insight into the mechanisms and wide-ranging effects of this devastating disease. We aim to demonstrate the ability of a label-free 3D quantitative phase imaging technology, called quantitative oblique back-illumination microscopy (qOBM), to provide insight into the effects of SCD on brain vasculature. Using qOBM, we quantitatively analyze the vasculature of freshly excised, but otherwise unaltered, whole mouse brains. We use Townes sickle transgenic mice, which closely recapitulate the pathophysiology of human SCD, and sickle cell trait mice as controls. Two developmental time points are studied: 6-week-old mice and 20-week-old mice. Quantitative structural and biophysical parameters of the vessels (including the refractive index (RI), which is linearly proportional to dry mass) are extracted from the high-resolution images and analyzed. qOBM reveals structural differences in the brain blood vessel thickness (thinner for SCD in particular brain regions) and the RI of the vessel wall (higher and containing a larger variation throughout the brain for SCD). These changes were only significant in 20-week-old mice. Further, vessel breakages are observed in SCD mice at both time points. The vessel wall RI distribution near these breaks, up to away from the breaking point, shows an erratic behavior characterized by wide RI variations. Vessel diameter, tortuosity, texture within the vessel, and structural fractal patterns are found to not be statistically different. As with vessel breaks, we also observe blood vessel blockages only in mice brains with SCD. qOBM provides insight into the biophysical and structural composition of brain blood vessels in mice with SCD. Data suggest that the RI may be an indirect indicator of vessel rigidity, vessel strength, and/or tensions, which change with SCD. Future ex vivo and in vivo studies with qOBM could improve our understanding of SCD.

Single-shot refractive index slice imaging using spectrally multiplexed optical transfer function reshaping.

The refractive index (RI) of cells and tissues is crucial in pathophysiology as a noninvasive and quantitative imaging contrast. Although its measurements have been demonstrated using three-dimensional quantitative phase imaging methods, these methods often require bulky interferometric setups or multiple measurements, which limits the measurement sensitivity and speed. Here, we present a single-shot RI imaging method that visualizes the RI of the in-focus region of a sample. By exploiting spectral multiplexing and optical transfer function engineering, three color-coded intensity images of a sample with three optimized illuminations were simultaneously obtained in a single-shot measurement. The measured intensity images were then deconvoluted to obtain the RI image of the in-focus slice of the sample. As a proof of concept, a setup was built using Fresnel lenses and a liquid-crystal display. For validation purposes, we measured microspheres of known RI and cross-validated the results with simulated results. Various static and highly dynamic biological cells were imaged to demonstrate that the proposed method can conduct single-shot RI slice imaging of biological samples with subcellular resolution.

Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

Optical diffraction tomography meets metrology — Measurement accuracy on cellular and subcellular level

Three-dimensional quantitative phase imaging (3D QPI) delivers volumetric information about the refractive index distribution within microobjects. Such imaging and measurement capabilities are of great interest for technical and biomedical applications, yet there are no standardized methods for testing and reporting metrological performance. Proper metrology is crucial for further developments in QPI and for cross-referencing a range of physical properties with other laboratories and techniques. In this paper we present methodology for metrological evaluation of the 3D QPI instruments for single-cell and subcellular structures. The methodology entails suitable phantom, its digital twin, quality assessment metric and easily reproducible protocol that is attainable for both numerical and experimental analysis. We demonstrate its applicability by comparing simulated reconstructions with measurement results from 3 optical diffraction tomography systems. The results will serve as a reference point for past and future research, encourage to benchmark the new systems in a similar manner and further the efforts towards the standardization in 3D QPI metrology.

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

Histopathological examination of blood cells plays a crucial role in the diagnosis of various diseases. However, it involves time-consuming and laborious staining procedures required for microscopic review by medical experts and is not directly applicable for point-of-care diagnosis in resource-limited locations. This study reports a dilution-, actuation- and label-free method for the analysis of individual red blood cells (RBCs) using a capillary microfluidic device and quantitative phase imaging. Blood, without any sample treatment, is directly loaded into a micrometer-thick channel such that it forms a quasi-monolayer inside the channel. The morphological and biochemical properties of RBCs, including hemoglobin concentration, hemoglobin content, and corpuscular volume, were retrieved using the refractive index tomograms of individual RBCs measured using 3D quantitative phase imaging. The deformability of individual RBCs was also obtained by measuring the dynamic membrane fluctuations. The proposed framework applies to other imaging modalities and biomedical applications, facilitating rapid and cost-effective diagnosis and prognosis of diseases.

Pupil-aberration calibration with controlled illumination for quantitative phase imaging.

Quantitative phase imaging (QPI) exploits sample-induced changes in the optical field to analyze biological specimens in a label-free manner. However, the quantitative nature of QPI makes it susceptible to optical aberrations. We propose a method for calibrating pupil aberrations by imaging a sample of interest. The proposed method recovers pupil information by utilizing the cross-spectral density between optical fields at different incident angles and allows both thin and weakly scattering three-dimensional samples for calibration. We experimentally validate the proposed method by imaging various samples, including a resolution target, breast tissue, and a polystyrene bead, and demonstrate aberration-free two- and three-dimensional QPI.

Three-dimensional phase optical transfer function in axially symmetric microscopic quantitative phase imaging

Three-dimensional quantitative phase imaging (3D QPI) is widely recognized as a potentially high-impact microscopic modality. Central to determining the resolution capability of 3D QPI is the phase optical transfer function (POTF). The magnitude of the POTF over its spatial frequency coverage (SFC) specifies the intensity of the response for each allowed spatial frequency. In this paper, a detailed analysis of the POTF for an axially symmetric optical configuration is presented. First, a useful geometric interpretation of the SFC, which enables its visualization, is presented. Second, a closed-form 1D integral expression is derived for the POTF in the general nonparaxial case, which enables rapid calculation of the POTF. Third, this formulation is applied to disk, annular, multi-annuli, and Gaussian illuminations as well as to an annular objective. Taken together, these contributions enable the visualization and simplified calculation of the 3D axially symmetric POTF and provide a basis for optimizing QPI in a wide range of applications.

Refractive Index Changes of Cells and Cellular Compartments Upon Paraformaldehyde Fixation Acquired by Tomographic Phase Microscopy.

Three‐dimensional quantitative phase imaging is an emerging method, which provides the 3D distribution of the refractive index (RI) and the dry mass in live and fixed cells as well as in tissues. However, an insufficiently answered question is the influence of chemical cell fixation procedures on the results of RI reconstructions. Therefore, this work is devoted to systematic investigations on the RI in cellular organelles of live and fixed cells including nucleus, nucleolus, nucleoplasm, and cytoplasm. The research was carried out on four different cell lines using a common paraformaldehyde (PFA)‐based fixation protocol. The selected cell types represent the diversity of mammalian cells and therefore the results presented provide a picture of fixation caused RI changes in a broader context. A commercial Tomocube HT‐1S device was used for 3D RI acquisition. The changes in the RI values after the fixation process are detected in the reconstructed phase distributions and amount to the order of 10−3. The RI values decrease and the observed RI changes are found to be different between various cell lines; however, all of them show the most significant loss in the nucleolus. In conclusion, our study demonstrates the evident need for standardized preparation procedures in phase tomographic measurements. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography.

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Three-dimensional quantitative phase imaging of blood coagulation structures by optical projection tomography in flow cytometry using digital holographic microscopy.

Blood coagulation is an important role in the hemostasis process. In the observation using microscopies, an aggregation structure of red blood cells indicates the degree of blood coagulation. Recently, it has been proposed that digital holographic microscopy (DHM) is a powerful tool for biomedical cell imaging on the basis of quantitative phase information. DHM has the advantage in that the real-time and three-dimensional (3-D) quantitative phase imaging can be realized in the wide field of view, which means that the 3-D morphological parameters of biological cells without a staining process are obtained in real time. We report the complete 3-D quantitative phase imaging of blood coagulation structures by optical projection tomography in a flow cytometry using DHM.

Three-dimensional quantitative phase imaging via tomographic deconvolution phase microscopy.

The field of three-dimensional quantitative phase imaging (3D QPI) is expanding rapidly with applications in biological, medical, and industrial research, development, diagnostics, and metrology. Much of this research has centered on developing optical diffraction tomography (ODT) for biomedical applications. In addition to technical difficulties associated with coherent noise, ODT is not congruous with optical microscopy utilizing partially coherent light, which is used in most biomedical laboratories. Thus, ODT solutions have, for the most part, been limited to customized optomechanical systems which would be relatively expensive to implement on a wide scale. In the present work, a new phase reconstruction method, called tomographic deconvolution phase microscopy (TDPM), is described which makes use of commercial microscopy hardware in realizing 3D QPI. TDPM is analogous to methods used in deconvolution microscopy which improve spatial resolution and 3D-localization accuracy of fluorescence micrographs by combining multiple through-focal scans which are deconvolved by the system point spread function. TDPM is based on the 3D weak object transfer function theory which is shown here to be capable of imaging "nonweak" phase objects with large phase excursions. TDPM requires no phase unwrapping and recovers the entire object spectrum via object rotation, mitigating the need to fill in the "missing cone" of spatial frequencies algorithmically as in limited-angle ODT. In the present work, TDPM is demonstrated using optical fibers, including single-mode, polarization-maintaining, and photonic-crystal fibers as well as an azimuthally varying CO2-laser-induced long-period fiber grating period as test phase objects.

Measurements of morphology and refractive indexes on human downy hairs using three-dimensional quantitative phase imaging.

We present optical measurements of morphology and refractive indexes (RIs) of human downy arm hairs using three-dimensional (3-D) quantitative phase imaging techniques. 3-D RI tomograms and high-resolution two-dimensional synthetic aperture images of individual downy arm hairs were measured using a Mach–Zehnder laser interferometric microscopy equipped with a two-axis galvanometer mirror. From the measured quantitative images, the RIs and morphological parameters of downy hairs were noninvasively quantified including the mean RI, volume, cylinder, and effective radius of individual hairs. In addition, the effects of hydrogen peroxide on individual downy hairs were investigated.

White-light diffraction tomography

A clever extension to a classic phase-contrast microscope allows speckle-free three-dimensional quantitative phase imaging of living cells in a tomographic imaging mode.