Image Stacks Research Articles

Universal inverse modeling of point spread functions for SMLM localization and microscope characterization.

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data.

Velocimetry of GHz elastic surface waves in quartz and fused silica based on full-field imaging of pump–probe reflectometry

This study reports an imaging method for gigahertz surface acoustic waves in transparent layers using infrared subpicosecond laser pulses in the ablation regime and an optical pump–probe technique. The reflectivity modulations due to the photoelastic effect of generated multimodal surface acoustic waves were imaged by an sCMOS camera illuminated by the time-delayed, frequency-doubled probe pulses. Moving the delay time between 6.0nsto11.5ns, image stacks of wave field propagation were created.Two representative samples were investigated: wafers of isotropic fused silica and anisotropic x-cut quartz. Rayleigh (SAW) and longitudinal dominant high-velocity pseudo-surface acoustic wave (HVPSAW) modes could be observed and tracked along a circular grid around the excitation center, allowing the extraction of angular profiles of the propagation velocity. In quartz, the folding of a PSAW was observed. A finite element simulation was developed to predict the measurement results. The simulation and measurement were in good agreement with a relative error of 2% to 5%.These results show the potential for fast and full-field imaging of laser-generated ultrasonic surface wave modes, which can be utilized for the characterization of thin transparent samples such as semiconductor wafers or optical crystals in the gigahertz frequency range.

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

Widefield Super-Resolution Infrared Spectroscopy and Imaging of Autofluorescent Biological Materials and Photosynthetic Microorganisms Using Fluorescence Detected Photothermal Infrared (FL-PTIR).

We have demonstrated high-speed, super-resolution infrared (IR) spectroscopy and chemical imaging of autofluorescent biomaterials and organisms using camera-based widefield photothermal detection that takes advantage of temperature-dependent modulations of autofluorescent emission. A variety of biological materials and photosynthetic organisms exhibit strong autofluorescence emission under ultraviolet excitation and the autofluorescent emission has a very strong temperature dependence, of order 1%/K. Illuminating a sample with pulses of IR light from a wavelength-tunable laser source causes periodic localized sample temperature increases that result in a corresponding transient decrease in autofluorescent emission. A low-cost light-emitting diode-based fluorescence excitation source was used in combination with a conventional fluorescence microscopy camera to detect localized variations in autofluorescent emission over a wide area as an indicator of localized IR absorption. IR absorption image stacks were acquired over a range of IR wavelengths, including the fingerprint spectral range, enabling extraction of localized IR absorption spectra. We have applied widefield fluorescence detected photothermal IR (FL-PTIR) to an analysis of autofluorescent biological materials including collagen, leaf tissue, and photosynthetic organisms including diatoms and green microalgae cells. We have also demonstrated the FL-PTIR on live microalgae in water, demonstrating the potential for label-free dynamic chemical imaging of autofluorescent cells.

In-fiber illumination-detection-synchronized confocal microscopy for high-precision fast axial scanning

Confocal microscopy remains a major workhorse in the 3D surface topography measurement owing to its reliability and compatibility for workpieces with various reflectance, which, however, suffers from slow scanning speed and limited axial resolution. Commercial confocal microscopy primarily relies on the axial movement of a sample or objective lens to create a series of 2D image stacks, and then reconstruct 3D map. Nevertheless, most samples and objective lens are too bulky to scan rapidly, let alone that multiple scans are required to increase the signal-to-noise ratio (SNR). To address this issue, an original approach named illumination-detection-synchronized confocal microscopy was developed, which enabled high-precision fast axial scanning via a compact fiber-multiplexed port with the high-speed synchronous motion of point source and point detection. The approach can effectively improve the peak localization precision while not requiring a high-precision motion stage. Theoretically, via controlling the magnification of the optical system M > 1, the total peak localization precision of axial light intensity response curve can be improved by M3 times. Experiments show that the single-point repeat measurement precision by this approach is improved by 4 times, and the profile measurement precision is enhanced by 2.3 times. Moreover, this approach can effectively avoid collisions between the sample and objective lens, and has the potential to design a low-cost single-point confocal sensor or confocal profilometer.

Assessing hurricane impact on vegetation and endangered deer habitat using airborne lidar and multispectral images

In the lower Florida Keys, the endangered Florida Key deer and numerous other wildlife species inhabit a vulnerable island environment susceptible to storm surges and rising seawater due to low elevation and flat terrain. Timely and reliable assessment of vegetation damage from natural disasters, such as Hurricane Irma, is crucial for effective habitat management. The study’s overall objective is to examine Hurricane Irma’s impact on vegetation on No Name Key, Florida, using remote sensing. The study relates the area change in vegetation obtained from remote sensing analysis to Florida Key deer population changes following the storm. The methodology involved performing a thematic change detection analysis using the following data sources: (1) aerial multispectral images (for pre- and post-Hurricane), (2) airborne lidar data (for pre- and post-Hurricane), (3) an existing vegetation map, and (4) soil data. A Support Vector Machine (SVM) image classification algorithm was applied to pre- and post-storm input image stacks to create pre- and post-Hurricane Irma vegetation maps. We were then able to obtain the area change information (for various vegetation categories) by performing the change detection analysis of the 2 SVM-classified images. The differences in areas following the storm were calculated for 7 affected vegetation types. Using the area change information following Hurricane Irma, we estimated the number of deer supported by the storm-affected vegetation. These estimated deer numbers, based on the area differences in post-Hurricane Irma vegetation types, were compared to observed deer numbers collected during the post-Hurricane Irma Texas A&M Natural Resources Institute (NRI) deer field survey. The results showed the following: mangroves had the largest negative area changes (area loss), followed by pinelands, hardwoods/hammocks, developed areas, and buttonwoods. Freshwater marshes had the largest positive area changes (area gain). The deer's preferred vegetation areas had decreased post-Hurricane Irma, resulting in a reduced deer population compared to pre-storm numbers. The predicted number of the Key deer post-Hurricane Irma fell within a 95% confidence interval of the observed deer population from the post-storm field survey. The study findings and techniques could be applied to study climate change impact, especially sea level rise. This methodology can be valuable in assessing the impact of storms on other wildlife species in similar environments. The applications and methodology are especially relevant considering the increasing frequency and intensity of storm surges and the accelerating rate of sea level rise.

CShaperApp: Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

AbstractCaenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development. In this study, we developed a desktop software CShaperApp to segment fluorescence‐labeled images of cell membranes and analyze cellular morphologies interactively during C. elegans embryogenesis. Based on the previously proposed framework CShaper, CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine‐tuned one adapted to their in‐house dataset. Experimental results show that it takes about 30 min to process a three‐dimensional time‐lapse (4D) dataset, which consists of 150 image stacks at a ∼1.5‐min interval and covers C. elegans embryogenesis from the 4‐cell to 350‐cell stages. The robustness of CShaperApp is also validated with the datasets from different laboratories. Furthermore, modularized implementation increases the flexibility in multi‐task applications and promotes its flexibility for future enhancements. As cell morphology over development has emerged as a focus of interest in developmental biology, CShaperApp is anticipated to pave the way for those studies by accelerating the high‐throughput generation of systems‐level quantitative data collection. The software can be freely downloaded from the website of Github (cao13jf/CShaperApp) and is executable on Windows, macOS, and Linux operating systems.

Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification

Abstract. The impact of artisanal mining in sub-Saharan Africa and other developing countries are often huge as they leave trails of environmental and socio-economic effects on the people and the environment. 123 Multi-temporal SAR data acquired between 2019 and 2023 was used. SARPROZ was used to co-register the images and other preprocessing steps like radiometric and geometric correction to transform the images from SAR coordinates to geographic coordinates after generating all the interferograms. A stack of the 122 coherence map was created. Unsupervised classification was implemented on the stack. Principal component analysis of dimensionality reductions method yielded a far better result than the other unsupervised cluster methods attempted, it showed a very high classification accuracy of the terrain. The principal component analysis worked by computing the covariance matrix of the stacked coherence map then performed eigen-decomposition on it to yield eigenvectors and eigenvalues, The eigenvectors corresponding to the largest eigenvalues represent the principal components. These principal components capture the directions of maximum variance, and the eigenvectors provides a reduced-dimensional representation of the image stack which is then used to reconstruct an approximation of the original image that captures the essential features of the original SAR data. Backscatter intensity of the SAR images processed for this study period for unsupervised change detection and land cover classification, delineated the different features and classes based on long term coherence values.

Molecular histopathology of matrix proteins through autofluorescence super-resolution microscopy

Extracellular matrix diseases like fibrosis are elusive to diagnose early on, to avoid complete loss of organ function or even cancer progression, making early diagnosis crucial. Imaging the matrix densities of proteins like collagen in fixed tissue sections with suitable stains and labels is a standard for diagnosis and staging. However, fine changes in matrix density are difficult to realize by conventional histological staining and microscopy as the matrix fibrils are finer than the resolving capacity of these microscopes. The dyes further blur the outline of the matrix and add a background that bottlenecks high-precision early diagnosis of matrix diseases. Here we demonstrate the multiple signal classification method-MUSICAL-otherwise a computational super-resolution microscopy technique to precisely estimate matrix density in fixed tissue sections using fibril autofluorescence with image stacks acquired on a conventional epifluorescence microscope. We validated the diagnostic and staging performance of the method in extracted collagen fibrils, mouse skin during repair, and pre-cancers in human oral mucosa. The method enables early high-precision label-free diagnosis of matrix-associated fibrotic diseases without needing additional infrastructure or rigorous clinical training.

Ultra high frequency ultrasound enables real-time visualization of blood supply from chorioallantoic membrane to human autosomal dominant polycystic kidney tissue

Ultra high frequency (UHF) ultrasound enables the visualization of very small structures that cannot be detected by conventional ultrasound. The utilization of UHF imaging as a new imaging technique for the 3D-in-vivo chorioallantoic membrane (CAM) model can facilitate new insights into tissue perfusion and survival. Therefore, human renal cystic tissue was grafted onto the CAM and examined using UHF ultrasound imaging. Due to the unprecedented resolution of UHF ultrasound, it was possible to visualize microvessels, their development, and the formation of anastomoses. This enabled the observation of anastomoses between human and chicken vessels only 12 h after transplantation. These observations were validated by 3D reconstructions from a light sheet microscopy image stack, indocyanine green angiography, and histological analysis. Contrary to the assumption that the nutrient supply of the human cystic tissue and the gas exchange happens through diffusion from CAM vessels, this study shows that the vasculature of the human cystic tissue is directly connected to the blood vessels of the CAM and perfusion is established within a short period. Therefore, this in-vivo model combined with UHF imaging appears to be the ideal platform for studying the effects of intravenously applied therapeutics to inhibit renal cyst growth.

Automated segmentation and recognition of C. elegans whole-body cells.

Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. The code is available at https://github.com/reaneyli/ASR.

Volume microscopic analysis of membrane contact sites in mouse kidney renal proximal tubule epithelial cells

Subcellular organelles communicate with one another and with the plasma membrane (PM). A wide variety of membrane contact sites (MCS) tether subcellular structures to one another and mediate inter-organelle communication and exchange. The nature, distribution and function of membrane contact sites within the segment-specific architecture of renal epithelial cells has only just begun to be elucidated. We have used advanced imaging techniques, including Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and machine learning-based segmentation, to create a comprehensive, high resolution, 3D and quantifiable volume microscopic atlas of the ultrastructure of proximal tubule epithelial cells of the mouse kidney. We applied this data set to identify and quantify MCS between the endoplasmic reticulum and mitochondria and the PM in these cells. FIB-SEM was performed on a 50 μm by 50 μm by 44 μm block of tissue prepared from the renal cortex of a perfusion fixed 12-week-old male mouse. OsO4-stained tissue was embedded in Durcupan and a 3D X-ray tomogram of the block was used to select a volume of interest that included extended lengths of multiple proximal tubule (PT). The volume was imaged by a customized enhanced FIB-SEM system. The X-Y scanning resolution was set to 4-nm, and 4-nm FIB milling was used to expose surfaces for successive imaging. Generation of the complete image stack required ~11,000 cycles of scanning/FIB ablation over ~4 weeks of uninterrupted imaging. Segmentation of the resultant image set was performed with Zeiss Arivis Pro technology. The boundary box for reconstruction had the dimensions of 29.2 μm x 24.8 μm x 8 μm with a volume of 5793.280 μm3 comprising of 2000 consecutive images at an isotropic voxel size of 4 nm. Deep learning segmentation teased apart 10 cells with distinct boundaries in the PT structure. The volume of the cells in our clipped volume ranged from 8.79 μm3 to 1438.53 μm3 with a median of 45.20 μm3. In one representative proximal tubule cell the volume of mitochondria makes up 84.91 μm3 (31.62%) of the cell volume and the volume of Endoplasmic Reticulum (ER) makes up 23.92 μm3 (8.91%) of the cell volume. The 3D reconstruction and quantitative analysis revealed that there is one dominant ER object accounting for most of the ER volume in the whole cell: i.e., the largest ER object takes up 22.37 μm3 or 93.5% of the ER volume in the cell. A post deep-learning processing algorithm used 3D object modifications to identify 11,109 3D MCS between mitochondria and ER in the 10 cells. The Mito-ER MCS ranged from 512 nm3 to 354,304 nm3 with a median volume of 1792 nm3. Visually we can see Mito-ER and Mito-PM MCS extending across a region of up to ~45 nm between the two organelles. Taken together, these data show that the basal lateral surfaces of PT cell PMs are generously invested with fenestrated smooth ER, as are mitochondria and peroxisomes. Extensive regions of direct contact between mitochondria and PM are also observed. The biochemical identities of these contacts and their physiological functions are being actively pursued. NIDDK DK072612 NIDDK DK120534. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

X-ray computed tomography tracking of the calcium chloride hexahydrate crystallisation process

The research conducted on phase change materials (PCMs) for latent thermal energy storages (LTESs) is continuously growing in terms of publications, highlighting the importance of this topic. In fact, PCMs present many advantages that could help the energy transition and reduce CO2 emissions, by enhancing the performance of existing systems and better exploiting renewable energy. Therefore, it is of crucial interest to develop new and reliable methods to control LTES. Differently from sensible thermal energy storages, in LTESs the stored thermal energy is not proportional to the temperature. To really have an insight into the level of charge of these storages, it is important to know the liquid fraction, i.e., the amount of the liquid phase with respect to the whole amount of PCM. X-ray computed tomography (XCT) is a technology that allows to non-intrusively “look inside” the materials. In the current study, it was used to analyse the calcium chloride hexahydrate crystallization. This transient process of calcium chloride hexahydrate was tracked with a sequence of XCT scans, one every 6 minutes, resulting in 3D image stacks that were processed to obtain the volumetric liquid fraction evolution over time. Repeatability tests were run to evaluate the reliability of the XCT technique and the volumetric liquid fraction data was used to validate a numerical model developed within ANSYS Fluent framework. XCT offers great opportunities to study the heat and mass transfer mechanisms underlying the main issues of phase change materials, like, for example, supercooling and salt hydrate segregation.

Generation of continuous and sparse space filling toolpath with tailored density for additive manufacturing of biomimetics

A method of generating a continuous toolpath that can be biased in a user-specified direction of travel is proposed for the fabrication of density-based functionally graded parts through Additive Manufacturing (AM). The methodology utilizes Lin Kernighan's (LK) Travelling Salesman Problem (TSP) solver over a digitized grid within the contour domain to generate a toolpath with minimal lifts and a common start and end point. Three force-based methods of digitization, namely rectangular, circular, and contour adaptive, are proposed in this work. Each of these methods initialize from a structured or an unstructured grid, where the grid points are assumed to be connected with either linear (rectangular digitization) or a combination of linear and torsional springs (circular and contour adaptive digitization). Enforcing an equilibrium amongst the spring forces and appropriately selecting the ideal spring length, the necessary configuration of grid points can be generated for a desired toolpath.The density of grid points (consequently, part density) can be varied through the user-defined input function or an image-based density map imposed on the ideal spring length over the contour domain. The proposed toolpath, as a case study, was implemented for printing a bone with density prescribed through a CT scan image stack. The CT scan of the printed part qualitatively establishes the conformity of the toolpath to the user-specified density gradient.

SUPERVISED IMAGE CLASSIFICATION MODEL FOR CORAL BLEACHING DETECTION USING A BI-TEMPORAL SENTINEL-2 IMAGE STACK

Abstract. Coral reefs are among the most vulnerable ecosystems to coastal and land-based anthropogenic factors. Aside from sudden increase in sea temperatures, external factors such as local and regional disturbances are found to influence coral reef environments which often lead to bleaching events. According to the Status of Coral Reefs of the World: 2020 report, from 2009 to 2018, there has been a progressive loss of live corals at the global level which may be attributed to the increasing anthropogenic activities (Souter et al., 2021). Due to coral’s sensitivity to environmental stressors, it is significantly considered as an indicator for global climate conditions. In this regard, this study developed a remote sensing change detection technique to segment coral bleaching from satellite images. Using a bi-temporal image stack composed of Sentinel-2 images showing pre and post bleaching, a machine learning classification model was developed to capture typologies of changes visible between the two images which included bleaching. Random Forest (RF) algorithm was employed to classify changes. This model obtained overall accuracies and kappa statistics of 0.97 and 0.94 respectively with minimum consumer and producer accuracy of 0.91. Moreover, the identified changes showed 78% agreement with the in-situ data composed of 31 monitoring stations distributed around Sekisei Lagoon, Okinawa, Japan. This study demonstrated a promising potential of machine learning for change detection for coral bleaching monitoring.

Virtual reality-empowered deep-learning analysis of brain cells

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.

Extracting focus variation data from coherence scanning interferometric measurements

Coherence scanning interferometry (CSI), based on the principle of interference, can achieve sub-nanometer precision for height measurements. On the other hand, focus variation microscopy (FVM), combining the small depth of field of the objective, is a widely used surface topography measurement method suited to surface topography that is mostly optically rough. In this paper, we propose a method to simultaneously obtain the interferometric fringe data and focus variation FVM image stack, from a single vertical scanning process, using a CSI instrument without any hardware modifications. Using a 3D Fourier transform, the FVM signal, looks takes the form of a “bowtie” and the CSI signal resembles two “umbrellas” that are separated in 3D K-space. The signal is recovered using a 3D inverse Fourier transform and the surface topography can be determined by fusing the CSI and FVM signals. Since both signals come from the same instrument and scanning process, there is no need for coordinate registration and data interpolation during the data fusion process. Our method combines the features of CSI and FVM measurement, thereby improving the robustness and data coverage of the measurement. An all-in-focus surface topography map can also be generated using this method. This focusing feature has the potential to significantly improve the defect detection and quality control ability of CSI instruments.

Location, speciation, and quantification of carbon in silica phytoliths using synchrotron scanning transmission X-ray microspectroscopy.

Phytoliths of biogenic silica play a vital role in the silicon biogeochemical cycle and occlude a fraction of organic carbon. The location, chemical speciation, and quantification of this carbon within phytoliths have remained elusive due to limited direct experimental evidence. In this work, phytoliths (bilobate morphotype) from the sugarcane stalk epidermis are sectioned with a focused ion beam to produce lamellas (≈10 × 10 μm2 size, <500 nm thickness) and probed by synchrotron scanning transmission X-ray microspectroscopy (≈100-200 nm pixel size; energies near the silicon and carbon K-absorption edges). Analysis of the spectral image stacks reveals the complementarity of the silica and carbon spatial distributions, with carbon found at the borders of the lamellas, in islands within the silica, and dispersed in extended regions that can be described as a mixed silica-carbonaceous matrix. Carbon spectra are assigned mainly to lignin-like compounds as well as to proteins. Carbon contents of 3-14 wt.% are estimated from the spectral maps of four distinct phytolith lamellas. The results provide unprecedented spatial and chemical information on the carbon in phytoliths obtained without interference from wet-chemical digestion.

Quantitative phase imaging with a compact meta-microscope

Quantitative phase imaging (QPI) based on the transport-of-intensity equation (TIE) is a powerful technique in label-free microscopy. The image stack required for a successful TIE-QPI is traditionally obtained by translating the object or image plane, and the optical elements used in the conventional TIE-QPI systems are usually bulky and cumbersome. Stable and compact TIE-QPI methods capable of non-motion optical zooming can significantly facilitate applications that demand portability. Here, we propose a non-motion TIE-QPI method based on a dispersive metalens. The dispersive nature of the metalens is utilized to provide a spectral focal tuning. With fixed object and image planes, seven through-focus intensity images are captured by changing the illumination wavelength. The QPI performance is validated by retrieving the surface phase profiles of a microlens array and a phase resolution target, showing a high phase detection accuracy (deviation less than 0.03 wavelength). Subsequently, we established a compact meta-microscope by integrating the metalens with a commercially available CMOS image sensor, which shows good performance in microscopic imaging of unstained bio-samples. Our approach, based on the large-dispersive metalens, facilitates a compact and robust QPI system for optical metrology and label-free microscopy.

Three-dimensional particle image velocimetry measurement through three-dimensional U-Net neural network

This paper proposes a light field (LF) three-dimensional (3D) particle image velocimetry (PIV) method based on a digital refocused algorithm and 3D U-Net neural network for 3D three-component (3D-3C) velocity measurement. A digital refocused algorithm is used to generate a stack of LF-refocused images of tracer particles for establishing the 3D U-Net. The 3D U-Net is then used for the 3D particle field reconstruction. Based on a pair of 3D particle fields, the 3D-3C velocity field is obtained through a 3D cross correlation algorithm. Numerical simulations and experiments are conducted to analyze the accuracy and efficiency of the proposed method. The simulation results show that the elongation along the depth direction and the efficiency of the 3D particle field reconstruction are improved by the 3D U-Net. The 3D U-Net also provides a better correlation coefficient. The experimental results show that the reconstruction time of the proposed method is ∼220 s which is 10 times faster than the LF tomographic PIV. This further demonstrates that the proposed method improves the reconstruction efficiency without affecting the accuracy of velocity measurement.