Versatile Imaging Research Articles

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

Perspectives on 2D materials for hybrid and beyond-Si image sensor applications

Abstract The complementary metal–oxide–semiconductor (CMOS) image sensor has become essential and ubiquitous in our daily lives as it is present in almost every pocket. As demand for compact, multifunction, and high-efficiency Internet of Things applications continues to rise, novel configuration designs and manufacturing methods, such as neural network integration and 3D stacking have been implemented to enhance the CMOS image sensor’s (CIS) performance. However, the progress of image sensors based on silicon CMOS technology would eventually be limited by the intrinsic optical, electrical, and mechanical properties of silicon material. This has led to the exploration of two-dimensional materials (2DMs) and the emergence of 2DMs as promising candidates for the next generation of optoelectronic devices. In this article, we discuss the current advancements and challenges associated with silicon CISs and the potential benefits of incorporating 2DMs in the image sensor. We highlight three critical opportunities for 2DMs, including Si CMOS/2DMs hybrid structure and direct growth techniques of 2DMs on Si for back-end-of-line integration, 2DMs-based neuromorphic photodetectors (PDs) and optical neural networks for in-image-sensor-processing, and curved image sensor based on 2DMs PDs for bionic detection. With the growing maturity of 2DM technologies, we anticipate that the device scaling and the increase of integration density of 2DM electronics in the image sensor will continue, leading to the development of highly efficient, compact, intelligent, and versatile 2DM image sensors in the near future.

SSCFormer: Revisiting ConvNet-Transformer Hybrid Framework From Scale-Wise and Spatial-Channel-Aware Perspectives for Volumetric Medical Image Segmentation.

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features. To address these two issues, we propose a ConvNet-Transformer hybrid framework named SSCFormer for accurate and versatile medical image segmentation. The intra-scale ResInception and inter-scale transformer bridge are designed to collaboratively capture the intra- and inter-scale features, facilitating the interaction of small-scale disparity information at a single stage with large-scale from multiple stages. Global attention and scaling attention are cleverly integrated from a spatial-channel-aware perspective. The proposed SSCFormer is tested on four different medical image segmentation tasks. Comprehensive experimental results show that SSCFormer outperforms the current state-of-the-art methods.

Photoacoustic imaging for three-dimensional bioprinted constructs

Bioimaging is used to inspect the successful growth and functional differentiation of cells in printed biomaterials, which are ultimately finalized into functional artificial tissues capable of replacing native tissues. While optical bioimaging techniques are commonly utilized, the current trend in three-dimensional (3D) bioprinting towards replicating complex 3D microarchitectures poses a challenge for conventional optical imaging techniques in providing clear cross-sectional images due to the opaque nature of tissue. Consequently, these limitations necessitate lengthy and destructive preparation processes, which are associated with sacrificing cell viability and damaging the bioprinted material. Photoacoustic imaging (PAI) is a versatile imaging technique that extends the advantages of the optical bioimaging technique to undiscovered depths enabled by its acoustic hybridity, making itself a promising tool for non-destructive imaging of 3D bioprinted constructs. In this review, we introduce the flexible spectral contrasts provided by PAI, which are potentially applicable to 3D-bioprinted constructs, and summarize bioprinting studies that functionally implement PAI for in vitro and in vivo assessments. Finally, we provide an outlook on practical considerations for the more complete integration of these two fields, anticipating more fruitful discoveries as bioprinting advances towards more complex hierarchies.

Single-molecule detection and super-resolution imaging with a portable and adaptable 3D-printed microscopy platform (Brick-MIC).

Over the past decades, single-molecule and super-resolution microscopy have advanced and represent essential tools for life science research. There is,however, a growing gap between the state-of-the-art and what is accessible to biologists, biochemists, medical researchers or labs with financial constraints. To bridge this gap, we introduce Brick-MIC, a versatile and affordable open-source 3D-printed micro-spectroscopy and imaging platform. Brick-MIC enables the integration of various fluorescence imaging techniques with single-molecule resolution within a single platform and exchange between different modalities within minutes. We here present variants of Brick-MIC that facilitate single-molecule fluorescence detection, fluorescence correlation spectroscopy and super-resolution imaging (STORM and PAINT). Detailed descriptions of the hardware and software components, as well as data analysis routines are provided, to allow non-optics specialist to operate their own Brick-MIC with minimal effort and investments. We foresee that our affordable, flexible, and opensource Brick-MIC platform will be a valuable tool for many laboratories worldwide.

Reinforced black widow algorithm with restoration technique based on optimized deep generative adversarial network

ABSTRACT Image restoration is used to develop the quality of image that is triggered by various noises and blurring. During this causes, certain areas in the images are vanished. The existing works does not provide sufficient restoration process with high accuracy. Therefore, a new image restoration system based on Optimized Deep Generative Adversarial Network (DGAN) with Reinforced Black Widow algorithm (BWOA) is proposed in this paper to increase the restoration accuracy and reducing the noises. At first, the input image is converted as gray scale image and the multi-scale edge information is removed as damaged area of an image by constructing a smooth function. Here, the extracted multi-scale edge information is given to the DGAN model. After that, the images are trained to create the best fake images through continuous play among generator and discriminator. Then, the detected images are restored in the original image with high accuracy. The hyper parameters of the DGAN are optimized by using the BWOA. The major objective of this paper is ‘to increase the restoration accuracy and the quality of the image by decreasing the noises occurred in the input image.’ The simulation process is performed on the MATLAB platform. The proposed DGAN-BWOA-IR attains higher restoration accuracy of 9.3%, higher PSNR value 74.589(db), SSIM 9.023% the proposed system is likened with the existing approaches, such as plug-and-play image restoration including deep denoiser prior (DCNN-IR), Learning enriched features for rapid image restoration with enhancement (LEF-IR), Exploiting deep generative prior for versatile image restoration with manipulation (GAN-IR), respectively.

Combining poly-epitope MoonTags and labeled nanobodies for signal amplification in cell-specific PET imaging in vivo

Immunorecognition provides an excellent basis for targeted imaging techniques covering a wide range from basic research to diagnostics and from single cells to whole organisms. Fluorescence- or radioisotope-labeled antibodies, antibody fragments or nanobodies enable a direct signal readout upon binding and allow for versatile imaging from microscopy to whole-body imaging. However, as the signal intensity directly correlates with the number of labeled antibodies bound to their epitopes (1:1 binding), sensitivity for low-expressing epitopes can be limiting for visualization. For the first time, we developed poly-epitope tags with multiple copies (1 to 7) of a short peptide epitope, specifically the MoonTag, that are recognized by a labeled nanobody and aimed at signal amplification in microscopy and cell-specific PET imaging. In transiently transfected HeLa cells or stably transduced A4573 cells we characterized complex formation and in vitro signal amplification. Indeed, using fluorescently and radioactively labeled nanobodies we found an approximately linear signal amplification with increasing numbers of epitope copies in vitro. To test the poly-epitope approach in vivo, A4573 tumor cells were injected subcutaneously into the shoulder of NSG mice, with A4573 tumor cells expressing a poly-epitope of 7 MoonTags on one side and WT cells on the other side. Using a [68Ga]-labeled NODAGA-conjugated MoonTag nanobody, we performed PET/CT imaging at day 8–9 after tumor implantation. Specific binding of a [68Ga]-labeled NODAGA-conjugated MoonTag nanobody was observed in 7xMoonTag tumors (1.7 ± 0.5%ID/mL) by PET imaging, showing significantly higher radiotracer accumulation compared to the WT tumors (1.1 ± 0.3%ID/mL; p < 0.01). Ex vivo gamma counter measurements confirmed significantly higher uptake in 7xMoonTag tumors compared to WT tumors (p < 0.001). In addition, MoonTag nanobody binding was detected by autoradiography which was spatially matched with histological analysis of the tumor tissues. In conclusion, we expect nanobody-based poly-epitope tag strategies to be widely applicable for multimodal imaging techniques given the advantageous properties of nanobodies and their amenability to genetic and chemical engineering.

Widefield in vivo imaging system with two fluorescence and two reflectance channels, a single sCMOS detector, and shielded illumination.

Widefield microscopy of the entire dorsal part of mouse cerebral cortex enables large-scale ("mesoscopic") imaging of different aspects of neuronal activity with spectrally compatible fluorescent indicators as well as hemodynamics via oxy- and deoxyhemoglobin absorption. Versatile and cost-effective imaging systems are needed for large-scale, color-multiplexed imaging of multiple fluorescent and intrinsic contrasts. We aim to develop a system for mesoscopic imaging of two fluorescent and two reflectance channels. Excitation of red and green fluorescence is achieved through epi-illumination. Hemoglobin absorption imaging is achieved using 525- and 625-nm light-emitting diodes positioned around the objective lens. An aluminum hemisphere placed between objective and cranial window provides diffuse illumination of the brain. Signals are recorded sequentially by a single sCMOS detector. We demonstrate the performance of our imaging system by recording large-scale spontaneous and stimulus-evoked neuronal, cholinergic, and hemodynamic activity in awake, head-fixed mice with a curved "crystal skull" window expressing the red calcium indicator jRGECO1a and the green acetylcholine sensor . Shielding of illumination light through the aluminum hemisphere enables concurrent recording of pupil diameter changes. Our widefield microscope design with a single camera can be used to acquire multiple aspects of brain physiology and is compatible with behavioral readouts of pupil diameter.

Emerging 2D Nanomaterials-Integrated Hydrogels: Advancements in Designing Theragenerative Materials for Bone Regeneration and Disease Therapy.

This review highlights recent advancements in the synthesis, processing, properties, and applications of 2D-material integrated hydrogels, with a focus on their performance in bone-related applications. Various synthesis methods and types of 2D nanomaterials, including graphene, graphene oxide, transition metal dichalcogenides, black phosphorus, and MXene are discussed, along with strategies for their incorporation into hydrogel matrices. These composite hydrogels exhibit tunable mechanical properties, high surface area, strong near-infrared (NIR) photon absorption and controlled release capabilities, making them suitable for a range of regeneration and therapeutic applications. In cancer therapy, 2D-material-based hydrogels show promise for photothermal and photodynamic therapies, and drug delivery (chemotherapy). The photothermal properties of these materials enable selective tumor ablation upon NIR irradiation, while their high drug-loading capacity facilitates targeted and controlled release of chemotherapeutic agents. Additionally, 2D-materials -infused hydrogels exhibit potent antibacterial activity, making them effective against multidrug-resistant infections and disruption of biofilm generated on implant surface. Moreover, their synergistic therapy approach combines multiple treatment modalities such as photothermal, chemo, and immunotherapy to enhance therapeutic outcomes. In bio-imaging, these materials serve as versatile contrast agents and imaging probes, enabling their real-time monitoring during tumor imaging. Furthermore, in bone regeneration, most 2D-materials incorporated hydrogels promote osteogenesis and tissue regeneration, offering potential solutions for bone defects repair. Overall, the integration of 2D materials into hydrogels presents a promising platform for developing multifunctional theragenerative biomaterials.

An empirical study on the robustness of the segment anything model (SAM)

The Segment Anything Model (SAM) is a foundation model for general image segmentation. Although it exhibits impressive performance predominantly on natural images, understanding its robustness against various image perturbations and domains is critical for real-world applications where such challenges frequently arise. In this study we conduct a comprehensive robustness investigation of SAM under diverse real-world conditions. Our experiments encompass a wide range of image perturbations. Our experimental results demonstrate that SAM’s performance generally declines under perturbed images, with varying degrees of vulnerability across different perturbations. By customizing prompting techniques and leveraging domain knowledge based on the unique characteristics of each dataset, the model’s resilience to these perturbations can be enhanced, addressing dataset-specific challenges. This work sheds light on the limitations and strengths of SAM in real-world applications, promoting the development of more robust and versatile image segmentation solutions. Our code is available at https://github.com/EternityYW/SAM-Robustness/.

Smartphone Imaging Device for Multimodal Detection of Hydrogen Sulfide Using Cu-Doped MOF Sensors.

Multimodal sensing platforms may offer reliable, fast results, but it is still challenging to incorporate biosensors with high discriminating ability in complex biological samples. Herein, we established a highly sensitive dual colorimetric/electrochemical monitoring approach for the detection of hydrogen sulfide (H2S) utilizing Cu-doped In-based metal-organic frameworks (Cu/In-MOFs) combined with a versatile color selector software-based smartphone imaging device. H2S can result in the enhancement of the electrochemical signal because of the electroactive substance copper sulfide (CuxS), the decrease of the colorimetric signal of the characteristic absorption response caused by the strong coordination effect on Cu/In-MOFs, and the obvious changes of red-green-blue (RGB) values of images acquired via an intelligent smartphone. Attractively, the Cu/In-MOFs-based multimodal detection guarantees precise and sensitive detection of H2S with triple-signal detection limits of 0.096 μM (electrochemical signals), 0.098 μM (colorimetric signals), and 0.099 μM (smartphone signals) and an outstanding linear response. This analytical toolkit provides an idea for fabricating a robust, sensitive, tolerant matrix and reliable sensing platform for rapidly monitoring H2S in clinical disease diagnosis and visual supervision.

Multimodal Multiphoton Tomography with a Compact Femtosecond Fiber Laser

Multiphoton tomography (MPT) based on near infrared (NIR) femtosecond laser technology has become a versatile high-resolution clinical and research imaging tool. We report on multimodal MPT with the air-cooled fiber laser tomograph MPTcompact. The ultracompact passively mode-locked erbium-doped 50/80 MHz laser operating at 780 nm is directly integrated into the 360° imaging head mounted on a flexible mechanical arm. The tomograph provides optical biopsies with subcellular resolution and optical metabolic imaging capability based on two-photon autofluorescence (AF), second harmonic generation, fluorescence lifetime imaging (FLIM) by time-correlated single photon counting with 250 ps temporal resolution, reflectance confocal microscopy, and white LED light CMOS camera imaging. For the first time, NIR femtosecond laser pulses have been used on human skin to realize simultaneous one-photon confocal imaging and two-photon imaging. Most useful information is provided by the two-photon excited intratissue AF of the coenzymes NAD(P)H and flavins. The signals of both types of coenzymes can be separated by FLIM. Furthermore, the free and protein-bound forms can be distinguished by time-resolved AF detection because the protein-bound NADH has one order higher AF lifetime than free (non-bound) NADH. The tomograph contains onboard storage batteries so that it can operate for up to a few hours independently from external power supply. Applications include in vivo cancer detection and in situ evaluation of anti-aging drugs and pharmaceuticals. The tomograph MPTcompact has been successfully tested in a clinical multicenter study for the diagnosis of malignant melanoma on 100 patients with suspicious pigmented lesions. MPT has the potential to realize non-invasive high-resolution label-free in vivo histology within minutes and, therefore, to reduce the number of physically taken biopsies.

NIRis: A low-cost, versatile imaging system for near-infrared fluorescence detection of phototrophic cell colonies used in research and education.

A variety of costly research-grade imaging devices are available for the detection of spectroscopic features. Here we present an affordable, open-source and versatile device, suitable for a range of applications. We provide the files to print the imaging chamber with commonly available 3D printers and instructions to assemble it with easily available hardware. The imager is suitable for rapid sample screening in research, as well as for educational purposes. We provide details and results for an already proven set-up which suits the needs of a research group and students interested in UV-induced near-infrared fluorescence detection of microbial colonies grown on Petri dishes. The fluorescence signal confirms the presence of bacteriochlorophyll a in aerobic anoxygenic phototrophic bacteria (AAPB). The imager allows for the rapid detection and subsequent isolation of AAPB colonies on Petri dishes with diverse environmental samples. To this date, 15 devices have been build and more than 7000 Petri dishes have been analyzed for AAPB, leading to over 1000 new AAPB isolates. Parts can be modified depending on needs and budget. The latest version with automated switches and double band pass filters costs around 350€ in materials and resolves bacterial colonies with diameters of 0.5 mm and larger. The low cost and modular build allow for the integration in high school classes to educate students on light properties, fluorescence and microbiology. Computer-aided design of 3D-printed parts and programming of the employed Raspberry Pi computer could be incorporated in computer sciences classes. Students have been also inspired to do agar art with microbes. The device is currently used in seven different high schools in Finland. Additionally, a science education network of Finnish universities has incorporated it in its program for high school students. Video guides have been produced to facilitate easy operation and accessibility of the device.

BiU-net: A dual-branch structure based on two-stage fusion strategy for biomedical image segmentation

Background and objectiveComputer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge to construct an effective medical image segmentation structure. Recently, hybrid architectures based on convolutional neural networks (CNNs) and transformers were proposed. However, most current backbones directly replace one or all convolutional layers with transformer blocks, regardless of the semantic gap between features. Thus, how to sufficiently and effectively eliminate the semantic gap as well as combine the global and local information is a critical challenge. MethodsTo address the challenge, we propose a novel structure, called BiU-Net, which integrates CNNs and transformers with a two-stage fusion strategy. In the first fusion stage, called Single-Scale Fusion (SSF) stage, the encoding layers of the CNNs and transformers are coupled, with both having the same feature map size. The SSF stage aims to reconstruct local features based on CNNs and long-range information based on transformers in each encoding block. In the second stage, Multi-Scale Fusion (MSF), BiU-Net interacts with multi-scale features from various encoding layers to eliminate the semantic gap between deep and shallow layers. Furthermore, a Context-Aware Block (CAB) is embedded in the bottleneck to reinforce multi-scale features in the decoder. ResultsExperiments on four public datasets were conducted. On the BUSI dataset, our BiU-Net achieved 85.50 % on Dice coefficient (Dice), 76.73 % on intersection over union (IoU), and 97.23 % on accuracy (ACC). Compared to the state-of-the-art method, BiU-Net improves Dice by 1.17 %. For the Monuseg dataset, the proposed method attained the highest scores, reaching 80.27 % and 67.22 % for Dice and IoU. The BiU-Net achieves 95.33 % and 81.22 % Dice on the PH2 and DRIVE datasets. ConclusionsThe results of our experiments showed that BiU-Net transcends existing state-of-the-art methods on four publicly available biomedical datasets. Due to the powerful multi-scale feature extraction ability, our proposed BiU-Net is a versatile medical image segmentation framework for various types of medical images. The source code is released on (https://github.com/ZYLandy/BiU-Net).

Single-Spin Sensing: A Molecule-on-Tip Approach.

Magnetometry plays a pivotal role in addressing the requirements of ultradense storage technology and overcoming challenges associated with downscaled spin qubits. A promising approach for atomic-scale single-spin sensing involves utilizing a magnetic molecule as a spin sensor, although such a realization is still in its early stages. To tackle this challenge and underscore the potential of this method, we combined a nickelocene molecule with scanning tunneling microscopy to perform versatile spin-sensitive imaging of magnetic surfaces. We investigated model Co islands on Cu(111) of different thicknesses having variable magnetic properties. Our method demonstrates robustness and reproducibility, providing atomic-scale sensitivity to spin polarization and magnetization orientation, owing to a direct exchange coupling between the nickelocene-terminated tip and the Co surfaces. We showcase the accessibility of magnetic exchange maps using this technique, revealing unique signatures in magnetic corrugation, which are well described by computed spin-density maps. These advancements significantly improve our capacity to probe and visualize magnetism at the atomic level.

Acoustofluidic scanning fluorescence nanoscopy with a large field of view

Large-field nanoscale fluorescence imaging is invaluable for many applications, such as imaging subcellular structures, visualizing protein interactions, and high-resolution tissue imaging. Unfortunately, conventional fluorescence microscopy requires a trade-off between resolution and field of view due to the nature of the optics used to form the image. To overcome this barrier, we developed an acoustofluidic scanning fluorescence nanoscope that simultaneously achieves superior resolution, a large field of view, and strong fluorescent signals. The acoustofluidic scanning fluorescence nanoscope utilizes the superresolution capabilities of microspheres that are controlled by a programmable acoustofluidic device for rapid fluorescence enhancement and imaging. The acoustofluidic scanning fluorescence nanoscope resolves structures that cannot be resolved with conventional fluorescence microscopes with the same objective lens and enhances the fluorescent signal by a factor of ~5 without altering the field of view of the image. The improved resolution realized with enhanced fluorescent signals and the large field of view achieved via acoustofluidic scanning fluorescence nanoscopy provides a powerful tool for versatile nanoscale fluorescence imaging for researchers in the fields of medicine, biology, biophysics, and biomedical engineering.

Electrode Placement Optimization for Electrical Impedance Tomography Using Active Learning

Electrical impedance tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. Herein, a novel systematic framework is presented that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user‐defined metrics. Given the many‐to‐one mapping between electrode configuration and the performance metric, the approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN's prediction accuracy for unseen electrode configurations, performance metrics are maximized while iteratively updating the NN via active learning during the optimization process. Transfer learning is employed to expedite optimization of electrode placements for time‐consuming iterative reconstruction techniques by fine‐tuning a NN initially trained on one‐step reconstruction data. The method is validated using two representative reconstruction methods: one‐step reconstruction with Newton's one‐step error reconstructor prior and the iterative total variation method. This research underscores the potential of the proposed framework in addressing EIT's inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of noninvasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT.

Compressive sensing-based multi-focus line-scanning two-photon microscopy for fast 3D imaging

Fast 3D volume imaging methods have been playing increasingly important roles in biological studies. In this article, we present the design and characterization of a multi-focus line-scanning two-photon microscope. Specifically, a digital micromirror device (DMD) is employed to generate a randomly distributed focus array on a plane (i.e., x-z) via binary holography. Next, a galvanometric mirror scans the focus array in a direction normal to the plane (i.e., y-axis) over the imaging volume. For sparse samples, e.g., neural networks in a brain, 1-3 foci are used together with compressive sensing algorithm to achieve a volume imaging rate of 15.5 volumes/sec over 77 × 120 × 40 µm3. High-resolution optical cross-sectional images on selected planes and regions can be generated by sequentially scanning the laser focus generated on the x-z plane with good imaging speeds (e.g., 107 frames/sec over 80 × 120 × 40 µm3). In the experiments, microbeads, pollens, and mouse brain slices have been imaged to characterize the point spread function and volume image rate and quality at different sampling ratios. The results show that the multi-focus line-scanning microscope presents a fast and versatile 3D imaging platform for deep tissue imaging and dynamic live animal studies.

Interferometric modulation for generating extended light sheet: improving field of view.

Light-sheet fluorescence microscopy (LSFM) has emerged as a powerful and versatile imaging technique renowned for its remarkable features, including high-speed 3D tomography, minimal photobleaching, and low phototoxicity. The interference light-sheet fluorescence microscope, with its larger field of view (FOV) and more uniform axial resolution, possesses significant potential for a wide range of applications in biology and medicine. The aim of this study is to investigate the interference behavior among multiple light sheets (LSs) in LSFM and optimize the FOV and resolution of the light-sheet fluorescence microscope. We conducted a detailed investigation of the interference effects among LSs through theoretical derivation and numerical simulations, aiming to find optimal parameters. Subsequently, we constructed a customized system of multi-LSFM that incorporates both interference light sheets (ILS) and noninterference light-sheet configurations. We performed beam imaging and microsphere imaging tests to evaluate the FOV and axial resolution of these systems. Using our custom-designed light-sheet fluorescence microscope, we captured the intensity distribution profiles of both interference and noninterference light sheets (NILS). Additionally, we conducted imaging tests on microspheres to assess their imaging outcomes. The ILS not only exhibits a larger FOV compared to the NILS but also demonstrates a more uniform axial resolution. By effectively modulating the interference among multiple LSs, it is possible to optimize the intensity distribution of the LSs, expand the FOV, and achieve a more uniform axial resolution.

Space‐Time Coding Metasurface For Multifunctional Holographic Imaging

AbstractHolographic imaging as one of promising image techniques reconstructs object image using the amplitude and phase information scattered from objects. Programmable metasurface composed of digitally coding unit cells can arbitrarily manipulate amplitude and phase of the scattering wave by tailoring operation states of the unit cells, and thus achieves high‐resolution imaging reconstruction. However, the conventional metasurface can only achieves a desired holographic image on an imaging plane. This paper presented a time‐space coding 2‐bit metasurface to overcome this challenge. By rapidly switching PIN diodes loaded in metasurface unit cell according to the customized time sequences, the magnitude and phase of the ±1st order harmonic fields can be fully controlled, achieving the hologram imaging at the higher order harmonics. The proof‐of‐concept measurements are given to demonstrate the versatile holographic imaging functions at the ±1st order harmonics including two different holographic images on an imaging plane, holographic imaging and beam scanning, and holographic imaging and orbital angular momentum (OAM) wave generation, which undoubtedly enriches the applications of the programmable metasurfaces in the field of the holographic imaging.