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International audience; This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.

PURPOSE: Previous cardiac imaging studies using hyperpolarized (HP) [1-(13)C]pyruvate were acquired at end-diastole (ED). Little is known about the interaction between cardiac cycle and metabolite content in the myocardium. In this study, we compared images of HP pyruvate and products at end-systole (ES) and ED. METHODS: A dual-phase (13)C MRI sequence was implemented to acquire two sequential HP images within a single cardiac cycle at ES and ED during successive R-R intervals in an interleaved manner. Each healthy volunteer (n = 3) received two injections of HP [1-(13)C]pyruvate for the dual-phase imaging on the short-axis (SA) and the vertical long-axis (LA) planes. Spatial distribution of HP (13)C metabolites at each cardiac phase was correlated to multi-phase (1)H MRI to confirm the mechanical changes. Ratios of myocardial HP metabolites were compared between ES and ED. Segmental analysis was performed on the mid-cavity SA plane. RESULTS: Besides mechanical changes, metabolic profiles of the heart detected by HP [1-(13)C]pyruvate differed between ES and ED. The myocardial signal of [(13)C]bicarbonate relative to [1-(13)C]lactate was significantly smaller at ED than the ratio at ES (p < 0.05), particularly in mid-anterior and mid-inferoseptal segments. The distinct metabolic profiles in the myocardium likely reflect the technical aspects of the imaging approach such as the coronary flow in addition to the cyclical changes in metabolism. CONCLUSION: The study demonstrates that metabolic profiles of the heart, measured by HP [1-(13)C]pyruvate, are affected by the cardiac cycle that the data is acquired in.

International audience; Infrared imaging finds numerous applications in airborne measurements, structural monitoring, medical diagnostics, and spectroscopy. Long wave infrared (LWIR) radiation (λ = 8-14 µm) enables self-illuminated thermal imaging, and can uniquely identify different chemical species. The investigation of thermal emission control using plasmonic antenna devices, 1 and the study of tunable free-form planar optics 2 has motivated our development of a novel high-resolution thermal imaging technique. Speckle imaging has been successfully used to image optical intensity or phase through complex inhomogeneous scattering media-particularly at visible wavelengths, 3 and recently in the infrared. 4 Single-shot high-resolution images of the scattered light capture sufficient information to reconstruct images through opaque media and around corners with diffraction-limited resolution. In this context, we have developed a high-resolution broadband speckle imaging setup in the LWIR for phase reconstruction, using a thin scattering medium in front of an uncooled microbolometric camera. Our method utilizes the large angular memory effect of a thin scattering medium. Local phase gradients within the incoming beam produce distorted speckle images after scattering by the scatterer's surface. Local translation shifts between the speckle patterns are estimated by a fast diffeomorphic image registration algorithm to obtain a phase gradient map. Integrating this gradient map in 2-D finally yields the wavefront profile. We demonstrate infrared phase image reconstruction using our broadband LWIR speckle imaging methodology, which promises future applications in imaging through visually opaque objects like semiconductor circuits, solid-state nanoelectronics, and infrared optical components, for defect monitoring.

The 50th anniversary of Dennis Gabor’s Nobel prize (1971) and the upcoming 60th anniversary of the work of both Yuri N. Denisyuk (1962) and Emmett N. Leith, Juris Upatnieks (1962) are excellent reasons to look back at the inventions and the great pioneers of holography. In the talk at first I present my personal look at the timeline of optical and digital holography with focus on 2D and 3D holographic microscopy. Next I will address several metrological problems connected with 2D and 3D quantitative phase imaging based on data gathered by means of digital holography microscopy, holographic tomography and combined holographic tomography and optical coherence tomography .

Phase microscopy is widely used to image unstained biological samples. However, most phase imaging techniques require transmission geometries, making them unsuited for thick sample applications. Moreover, when applied to volumetric imaging, phase imaging generally requires large numbers of measurements, often making it too slow to capture live biological processes with fast 3D index-of-refraction variations. By combining oblique back-illumination microscopy and a z-splitter prism, we perform phase imaging that is both epi-mode and multifocus, enabling high-speed 3D phase imaging in thick, scattering tissues with a single camera. We demonstrate here 3D qualitative phase imaging of blood flow in chick embryos over a field of view of 546 × 546 × 137 μm3 at speeds up to 47 Hz.

Significance: Real-time monitoring of the heart rate and blood flow is crucial for studying cardiovascular dysfunction, which leads to cardiovascular diseases. Aim: This study aims at in-depth understanding of high-speed cardiovascular dynamics in a zebrafish embryo model for various biomedical applications via frequency-comb-referenced quantitative phase imaging (FCR-QPI). Approach: Quantitative phase imaging (QPI) has emerged as a powerful technique in the field of biomedicine but has not been actively applied to the monitoring of circulatory/cardiovascular parameters, due to dynamic speckles and low frame rates. We demonstrate FCR-QPI to measure heart rate and blood flow in a zebrafish embryo. FCR-QPI utilizes a high-speed photodetector instead of a conventional camera, so it enables real-time monitoring of individual red blood cell (RBC) flow. Results: The average velocity of zebrafish’s RBCs was measured from 192.5 to 608.8 μm∕s at 24 to 28 hour-post-fertilization (hpf). In addition, the number of RBCs in a pulsatile blood flow was revealed to 16 cells/pulse at 48 hpf. The heart rates corresponded to 94 and 142 beats-per-minute at 24 and 48 hpf. Conclusions: This approach will newly enable in-depth understanding of the cardiovascular dynamics in the zebrafish model and possible usage for drug discovery applications in biomedicine. National Research Foundation (NRF) Published version Funding: National Research Foundation of the Republic of Korea (NRF-2012R1A3A1050386, NRF-2020R1A2C2102338, NRF-2020R1A2C210233811, and NRF-2021R1A4A1031660); KAIST UP Program; Singapore National Research Foundation (NRF-NRFF2015-02).

Benefiting from two-wavelength information multiplexed into one hologram, two-wavelength off-axis holography can perform simultaneous quantitative phase imaging in an extended thickness range. However, its present phase processing still adopts the traditional Fourier-based method at the expense of the reconstruction speed. Hence, we propose a fast approach by combining critical sampling with division multiplexing. In our approach, the critical sampling is used to remove the redundant data, and the division multiplexing is developed to eliminate the redundant calculation processes. Experimental results illustrate that our approach can yield faster reconstruction with frame rates up to 46 fps even for one megapixel multiplexed holograms at two wavelengths on a single-core personal computer.

An earlier incorrect version appeared online. This article was corrected on December 17, 2018.

Quantitative phase imaging (QPI) is an emerging label-free modality that attracts significant interest in biomedicine in general and neuroscience in particular. Based on the principle of interferometry, QPI precisely maps the optical pathlength induced by the sample, and, thus, can visualize extremely transparent samples. The QPI field has grown rapidly in the past decade, and reliable instruments have been developed for in-depth biological studies. One particular figure of merit associated with QPI techniques describes the temporal phase sensitivity of instruments. Recently, several common path interferometry methods have been developed, which yield high stability and nanometer scale pathlength sensitivity. In neuroscience, QPI has shown unique capabilities in quantifying neural growth and dynamics in cell cultures, as well as high contrast imaging of brain tissue slices. In this paper, we review the principles of QPI, novel QPI technology, advances in data processing, and a number of exciting applications in neuroscience.

Twisted vector light beams (optical vortices) arise from a spiral modulation of the geometric (Pancharatnam-Berry) phase converting the light spin to the orbital angular momentum. The preferred geometric-phase elements using liquid crystals and plasmonic metasurfaces realize this conversion by structuring their building blocks, i.e., precisely orienting individual crystal molecules or plasmonic nanoantennas. Here, an analogous mechanism is discovered in the spiral phase modulation of light reflected by dielectric spheres and first demonstrated in natural phenomena, namely in the rainbow formation. The spiral geometric phase is documented by holographic imaging of full circle primary and secondary rainbows created in the laboratory. The measurement uses a wide-angle holographic camera (field of view approximate to 120 degrees) taking time-resolved self-correlation holograms (300 ms). The holograms allow a quantitative restoration of the spiral geometric phase of light reflected from thousands of randomly falling water drops. The capability of individual drops to generate vector vortex beams under circularly polarized illumination is proven theoretically and demonstrated in experiments using glass microspheres. The spherical reflectors are discovered as simple generators of vector vortex beams and vortex arrays, inspiring novel geometric-phase elements.

BackgroundChronic active MS lesions with paramagnetic rim can be identified by high-pass filtered (HPF) phase imaging or quantitative susceptibility mapping (QSM).PurposeThe objective was to compare the ability of HPF and QSM to identify MS lesions with greater myelin damage and to distinguish MS patients with increased clinical disability.Material and MethodsEighty-six patients were scanned with gradient echo sequence for lesion rim detection and FAST-T2 sequence for myelin water fraction (MWF) mapping. Chronic lesions were classified based on the presence/absence of rim on HPF and QSM images (HPF rim+/QSM rim+, HPF rim+/QSM rim-, HPF rim-/QSM rim+, HPF rim-/QSM rim-). A lesion-level linear mixed-effects model with MWF as outcome was used to compare myelin damage among the lesion groups. A multivariate patient-level linear regression model was fit to establish the association between Expanded Disease Status Scale (EDSS) and the number of rim lesions (zero vs. one or more).ResultsOf 2229 lesions, 96 (8.8%) were HPF rim+/QSM rim+, 211 (9.5%) were HPF rim+/QSM rim-, and the remainder had no rim. Adjusting for other factors, HPF rim+/QSM rim+ lesions had on average significantly lower MWF than both HPF rim+/QSM rim-(pConclusionQSM identifies chronic MS lesions with paramagnetic rim that on average have greater myelin damage. QSM may be a valuable tool for studying the impact of rim lesions on clinical disability in MS.

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light (520±36 nm) phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate (a) four phase-shifted frames and (b) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.

Pebrine is the most dreaded infectious disease of the silkworm and has devastated sericulture in Europe during the 18th century. Thereafter, if it is detected, the crop is burned to prevent further dissemination. The conventional microscopic examination of moth's body fluid is erroneous and it exacerbates on Metarhizium anisopliae (MA) contaminated test samples. This is due to the resemblance of pebrine and MA spores in the microscopic examination. Therefore, this study aims to demonstrate an efficient pebrine detection technique. In the proposed method, a motorised brightfield microscope is custom-made to acquire focused and defocused images of test spores. These images are used to produce quantitative phase images of the spores by the transport of intensity equation method. The phase images' histogram of oriented gradients feature is used by a machine learning classifier to categorise the spores. This system classified 92 pebrine and 185 MA spores with an accuracy of 97 at 0.04 seconds/spore. The duration taken for image acquisition is 2.5 minutes per sample (10 fields of view covering an area of 302 A� 260 I¼m2). The proposed method shows reliable results in pebrine diagnosis and would be an efficient alternative for current approaches. © 2021 Wiley-VCH GmbH

The ability to visualise transparent objects such as live cells is central to understanding biological processes. Here we experimentally demonstrate a novel nanostructured coverslip that converts phase information to high-contrast intensity images. This compact device enables real-time, all-optical generation of pseudo three-dimensional images of phase objects on transmission. We show that by placing unstained human cancer cells on the device, the internal structure within the cells can be clearly seen. Our research demonstrates the significant potential of nanophotonic devices for integration into compact imaging and medical diagnostic devices., The nanophotonics enhanced coverslip (NEC) enables ultra-compact phase imaging of samples placed directly on top of the device. Visualization of artificial phase objects and unstained biological cells is demonstrated.

We have proposed a new configuration of the single-shot, dual-wavelength, common-path, off-axis DHM system. The proposed system is used for decoupling the refractive index and thickness of a biological specimen.

This feature issue of Applied Optics is dedicated to the international meeting of Information Photonics 2020 (IP’20), which was held September 11–12, 2020, in Taipei, Taiwan. IP’20 covered a broad range of topics, including advanced display techniques, optical computing, and optical storage. This feature issue, however, limits topics to unconventional imaging techniques, such as digital holography, artificial-intelligence associated imaging, compressive imaging, and single-pixel imaging.

Significance: Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells. Aim: However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated. Approach: We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA). Results: With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient. Conclusions: It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.

Dual-comb microscopy (DCM), an interesting imaging modality based on the optical-frequency-comb (OFC) mode and image pixel one-to-one correspondence, benefits from scan-less full-field imaging and simultaneous confocal amplitude and phase imaging. However, the two fully frequency-stabilized OFC sources requirement hampers DCM practicality due to the complexity and costs. Here, a bidirectional single-cavity dual-comb fiber laser (SCDCFL) is adopted as a DCM low-complexity OFC source. Computational image correction reduces the image blur caused by the SCDCFL residual timing jitter. Nanometer-order step surface profilometry with a 14.0 nm uncertainty highlights the image-corrected DCM effectiveness. The proposed method enhances the DCM versality and practicality., Comment: 25 pages, 7 figures

The demand for single-shot and common-path holographic systems has become increasingly important in recent years, as such systems offer various advantages compared to their counterparts. Single-shot holographic systems, for example, reduce computational complexity as only a single hologram with the object information required to process, making them more suitable for the investigation of dynamic events; and common-path holographic systems are less vibration-sensitive, compact, inexpensive, and high in temporal phase stability. We have developed a single-shot common-path off-axis digital holographic setup based on a beam splitter and pinhole. In this paper, we present a concise review of the proposed digital holographic system for several applications, including the quantitative phase imaging to investigate the morphological and quantitative parameters, as a metrological tool for testing of micro-optics, industrial inspection and measurement, and sound field imaging and visualization.

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.

International audience

To measure the metastatic or mechanical properties of cancer cells, various methods have been implemented. However, these methods have some shortcomings that they are time-consuming, require direct contact with cells, and are measuring accuracy is sensitive to boundary conditions of the sample. To overcome these shortcomings, in this study, we developed a cell mechanical property measurement system based on the quantitative phase imaging system and ultrasound stimulation system capable of the precise determination of the invasion potential of cancer cells. By using a developed system, it is possible to successfully measure the change in thickness due to the ultrasound stimulation of various types of cancer cells according to various acoustic pressure. In addition, the time to recover the original thickness after ultrasound stimulation was also measured with a high-speed camera to characterize the mechanical properties of the cancer cells.

We develop a chemically-selective quantitative phase imaging technique based on mid-infrared photothermal effect, with which we can simultaneously measure a morphologically sensitive quantitative phase image and a molecularly sensitive mid-infrared photothermal phase image. We show that the wide-field label-free dual-modal microscopy technique can be implemented with digital holography (2D) and optical diffraction tomography (3D). We also demonstrate a new technique to improve the sensitivity of the mid-infrared photothermal phase imaging via a dynamic-range expanded quantitative phase imaging, called adaptive dynamic range shift quantitative phase imaging (ADRIFT-QPI).

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 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.

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.

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.

Quantitative phase imaging (QPI) and 3D refractive index (RI) tomography are important tools in biomedicine that yield label-free access to cellular and subcellular structures with unprecedented clarity. However, implementation of these technologies involves a transmission-based geometry which requires thin samples. Here we describe quantitative oblique back illumination microscopy (qOBM), which overcomes this significant limitation and achieves epiillumination quantitative phase imaging and 3D RI tomography in thick samples, including intact thick tissues. In this presentation, we will describe the method in detail and show results from thick samples including intact whole mouse brains and organoids. qOBM opens the door to many exciting new applications in biomedicine.

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.

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.

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.

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.

For the example of digital holographic microscopy (DHM) we explored strategies to discriminate adherent and suspended single cells utilizing biophysical parameters retrieved label-free from DHM quantitative phase images in combination with machine learning (ML). Quantitative DHM phase contrast images of adherent cells were segmented while suspended single cells were analyzed based on a two-dimensional fitting approach. The retrieved parameter clouds were subsequently evaluated with different ML algorithms with the aim of an intuitive and user-friendly data representation. The results of the study demonstrate that our approach is capable for reliable discrimination between different cell types and to distinguish between different phenotypes.

The first-line treatment for brain cancer is surgery, which focuses on maximizing the percentage of the tumor removed during surgery (i.e., extent of resection) while minimizing damage to healthy brain tissue. Data show that extent of resection is one of the most critical factors associated with prolonged survival. However, differentiating between tumor and healthy tissue intraoperatively remains a significant clinical challenge, resulting in an exceedingly low 5-year survival rate of only ~35%. In this work, we show that quantitative oblique back illumination microscopy (qOBM), a novel label-free optical imaging technique that achieves tomographic quantitative phase imaging (QPI) in thick scattering samples, clearly differentiates between tumor and healthy tissue. Using a 9L gliosarcoma rat tumor model, we show that quantitative image features from qOBM provide a robust set of biomarkers for disease. In addition, tumor regions, including diffuse tumor, and healthy brain structures, show excellent structural agreement with H&E stained and sliced brightfield images, the gold standard for cancer detection. The unique attribute of qOBM—low-cost, easy-to-use, label-free, and real-time—make this technology ideally suited to help guide neurosurgery and address this important unmet need. Here we describe our free-space qOBM system and present quantitative results from the 9L gliosarcoma rat tumor model.