Image Projection Research Articles

Projection of visual material on postoperative delirium in patients undergoing cardiac surgery: A double blind randomized clinical trial.

Postoperative delirium is a neurobehavioral disorder that can appear after surgery. It is characterized by an altered level of consciousness and impaired cognitive function. The aim of this study was to evaluate the impact of visual projection of images of relatives or loved ones in patients undergoing cardiac surgery in the immediate postoperative period, and its influence on the incidence and development of postoperative delirium. A randomized, double-blind clinical trial was designed in the immediate postoperative period of adult patients undergoing cardiac surgery. Consolidated Statement of Reporting Trials guidelines were followed. A control group (CG) and an intervention group (IG) were established. In the IG, the patients underwent a visual projection, while the usual unit treatment was carried out with the CG. Sociodemographic, anthropometric, anesthetic, and surgical variables were also recorded. The postoperative delirium assessment scale used was the confusion assessment method for diagnosing delirium in intensive care unit patients. Information was collected from 104 patients undergoing cardiac surgery. Most of the patients included in the study were men (66.35%) and the most performed surgical intervention was aortic valve replacement (34.62%). In the CG, positive patients in postoperative delirium increased from 19.23% to 25%, while in the IG they decreased from 5.77% to 1.92%. The logistic regression analysis presents a prediction model where the variables that influence the model are gender and group membership, meaning that being female and belonging to the IG significantly reduce the presence of delirium. The projection of visual material reduced the incidence of postoperative delirium in patients undergoing cardiac surgery, although it cannot be established that it is effective as a treatment once the pathology is already established.

Finite-element Adaptive Meshing Statistics of Liquid Crystal Coaxial Phase Shifters for mmW Electronics and THz Photonics Beyond Display: A Comparative Study

Sub-mmW and THz frequencies offer ultra-high-speed communications for next-generation 5G/6G networks, wherein research advancing the understanding will benefit the electronics and photonics community. Specifically, meshing resolutions across varying frequency ranges and material properties are central to the solution accuracy, reliability, and cost (memory and time) of computational mmW and THz simulations, particularly for the emerging reconfigurable coaxial phase shifters employing nematic liquid crystals (LCs) as tunable media. In the present study, a comparative meshing statistics analysis is conducted for two devices designed for 60 GHz and 0.3 THz, respectively. Each design features two distinct tuning states (LC permittivity of 2.754 and 3.3, respectively), all pertinent to the coaxial TEM (Transverse Electromagnetic) mode. By quantifying the broadband meshing and solution statistics of diverse frequencies and dielectric tuning states for the first time, we establish memory-conserving computational metrology involving reconfigurable coaxial devices operationalized with LC-filled tunable dielectrics tailored for mmW electronics and THz photonics. Full Text: PDF References M. Makowski, I. Ducin, K. Kakarenko, J. Suszek, A. Kowalczyk, "Performance of the 4k phase-only spatial light modulator in image projection by computer-generated holography", Phot. Lett. Poland 8, 1 (2016). CrossRef J.F. Li, H.R. Li, "Liquid Crystal-Filled 60 GHz Coaxially Structured Phase Shifter Design and Simulation with Enhanced Figure of Merit by Novel Permittivity-Dependent Impedance Matching", Electronics 13, 3 (2024). CrossRef J.F. Li, H.R. Li, "Modeling 0.3 THz Coaxial Single-Mode Phase Shifter Designs in Liquid Crystals with Constitutive Loss Quantifications", Crystals 14, 4 (2024). CrossRef J.F. Li, D.P. Chu, "Liquid Crystal-Based Enclosed Coplanar Waveguide Phase Shifter for 54–66 GHz Applications", Crystals 9, 12 (2019). CrossRef Z. Cendes, "The development of HFSS", USNC-URSI Radio Science Meeting (Fajardo, IEEE 2016). CrossRef T. Vaupel, "Accuracy And Modeling Improvements For An Integral Equation Framework Applied To Thin Layer Microstrip And Substrate Integrated Waveguide (Siw) Structures", EuCAP (Copenhagen, IEEE 2020). CrossRef L.J. Jiang, W.C. Chew, "Low-frequency fast inhomogeneous plane-wave algorithm (LF-FIPWA)", Microwave Opt. Tech. Lett. 40, 2 (2004). CrossRef J. Zhu, D. Jiao, "Eliminating the Low-Frequency Breakdown Problem in 3-D Full-Wave Finite-Element-Based Analysis of Integrated Circuits", IEEE Trans. Microw. Theory Tech. 58, 10 (2010). CrossRef

BI‐LAVA: Biocuration With Hierarchical Image Labelling Through Active Learning and Visual Analytics

AbstractIn the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labelled data and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi‐year collaboration with biocurators and text‐mining researchers, we derive an iterative visual analytics and active learning (AL) strategy to address these challenges. We implement this strategy in a system called BI‐LAVA—Biocuration with Hierarchical Image Labelling through Active Learning and Visual Analytics. BI‐LAVA leverages a small set of image labels, a hierarchical set of image classifiers and AL to help model builders deal with incomplete ground‐truth labels, target a hierarchical taxonomy of image modalities and classify a large pool of unlabelled images. BI‐LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections and neighbourhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human–machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labelled and unlabelled collections.

Optimization and correction of breast dynamic optical imaging projection data based on deep learning

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) technology, recognized for its non-invasive and radiation-free properties, is extensively utilized for the early screening and quantitative analysis of breast tumors. The integration of deep learning, a robust technology for automatic image feature extraction, with breast DOI has the potential to enhance tumor detection and diagnosis significantly. This paper introduces a deep learning-enhanced image optimization approach to overcome challenges such as poor image quality and distorted projection data commonly encountered in existing DOI methods. The approach utilizes convolutional neural networks (CNNs) to extract features from raw images and employs generative adversarial networks (GANs) to enhance these images, thereby improving their quality and contrast. Additionally, a novel correction algorithm is developed to address projection data distortion, enabling the reconstruction and correction of this data for more accurate and reliable imaging results. Experimental findings confirm that the proposed method markedly enhances both image quality and projection data accuracy in breast DOI, offering a reliable foundation for clinical diagnosis. This study not only provides a new perspective and methodology for the early screening and diagnosis of breast cancer but also holds substantial clinical importance and prospective applications.

RayStation/GATE Monte Carlo simulation framework for verification of proton therapy based on the 12N imaging

Objective.12N, having a half-life of 11 ms, is a highly effective positron emitter that can potentially provide near real-time feedback in proton therapy. There is currently no framework for comparing and validating positron emission imaging of12N. This work describes the development and validation of a Monte Carlo (MC) framework to calculate the images of12N, as well as long-lived isotopes, originating from activation by protons.Approach. The available dual-panel Biograph mCT PET scanner was modeled in GATE and validated by comparing the simulated sensitivity map with the measured one. The distributions of12N and long-lived isotopes were calculated by RayStation and used as the input of GATE simulations. The RayStation/GATE combination was verified using proton beam irradiations of homogeneous phantoms. A 120 MeV pulsed pencil beam with 108protons per pulse was used. Two-dimensional images were created from the GATE output and compared with the images based on the measurements and the 1D longitudinal projection of the full 2D image was used to calculate the12N activity range.Main results. The simulated sensitivity in the center of the FoV (5.44%) agrees well with the measured one (5.41%). The simulated and measured 2D sensitivity maps agree in good detail. The relative difference between the measured and simulated positron activity range for both12N and long-lived isotopes is less than 1%. The broadening of the12N images relative to those of the longer-lived isotopes can be understood in terms of the large positron range of12N.Significance. We developed and validated a MC framework based on RayStation/GATE to support the in-beam PET method for quality assurance of proton therapy. The inclusion of the very short-lived isotope12N makes the framework useful for developing near real-time verification. This represents a significant step towards translating12N real-time in vivo verification to the clinic.

Design and application of omnidirectional mirror for catadioptric optical system dedicated to high-speed video registration of lightning

Omnidirectional optical mirrors are widely used in many scientific, industrial and military applications but there is still a lot to do to improve their projection characteristics. The paper presents design method for a novel class of omnidirectional mirrors characterized by their settable reflection angle and 3D/2D image projection. Such approach can effectively improve the mirrored image distribution on matrix of optical sensor. A complex mirror curvature was manufactured with application of computerized numerical control milling machines and Physical Vapor Deposition technique. Three prototypes with 15°, 30° and 45° vertical top view angles were tested. Mirrors were applied to get a set of wide range cloud-to-ground lightning video registrations. Analysis of the high-speed video confirmed design assumptions, and using lightning location system data, enabled dimensioning and measurement of the lightning channel geometry in 3D. Proposed method can be easily implemented in other applications involving omnidirectional observation and photogrammetry.

Standard radiographic images in cephalomedullary nailing fixation for intertrochanteric femoral fractures

To review and summarize the projections of radiographic images during cephalomedullary nailing fixation for intertrochanteric femoral fractures, and to propose a set of three projections as standard requirement in immediate postoperative fluoroscopy. Papers on intertrochanteric femoral fractures treated with cephalomedullary nailing fixation that published in a three-year period of 2021-2023 in four leading English orthopedic trauma journals were searched in PubMed. The presented radiographic pictures were identified and scrutinized as whether they were in standard anteroposterior and/or lateral projections of the implanted nails. The nonstandard presence percentage was calculated. Combined with clinical experience, the standard anteroposterior and lateral perspective images of femoral neck, the current situation of radiographic imaging in the operation of cephalomedullary nails, the literature analysis of nonstandard images, the impact of limb rotation on image interpretation, and the characteristics of anteromedial 30° oblique perspective were summarized and analyzed. The presence of nonstandard radiographic pictures is 32.1% in anteroposterior view and 69.2% in lateral view in leading orthopedic trauma journals. In cephalomedullary nailing fixation operation of intertrochanteric femoral fractures, it is reasonable to use the radiographic images of the implanted nails to represent the fractured head-neck, as the head-neck implant (lag screw or helical blade) is aimed to put into centrally in femoral head in lateral projection. Limb rotation or nonstandard projections produced distortion of images, which interfers the surgeons' judgement of fracture reduction quality and the measurement of implant position parameters in femoral head (such as neck-shaft angle and tip-apex distance), and finally lead to a meaningless comparison with the accurate normal value. The 30° anteromedial oblique view from the true lateral (set as 0°) is a tangential projection of the cortices at the anteromedial inferior corner, which gives a clear profile for the determination of cortical apposition status and mechanical support. It is essential to get firstly the true standard lateral fluoroscopy of the nail (shown as a line), then rotate the C-arm to 90° and 30° to get anteroposterior and anteromedial oblique views, and use these three immediate postoperative radiographies as the baseline for evaluation of operative quality and follow-up comparisons. As for real-time monitoring of surgical steps, intraoperative fluoroscopy follows the "Enough is Good" principle, but as for immediate postoperative data storage and basis for operative quality evaluation and baseline for follow-up comparison, it is recommended to obtain a set of three standard radiographic pictures in anteroposterior, true lateral, and 30° anteromedial oblique fluoroscopic projections.

Predicting cell cycle stage from 3D single-cell nuclear-stained images.

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models-a support vector machine (SVM) and a deep neural network-to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08-0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.

Urban parks in developing countries: Challenges and opportunities in Addis Ababa, Ethiopia

The scarcity of well-functioning green spaces in the rapidly growing urban centers of developing countries poses significant challenges, hindering the well-being and quality of life for urban populations. Urban parks, often referred to as the "lungs of the city," have the potential to alleviate these challenges, yet their benefits are not always evident. This study explores the challenges and opportunities faced by urban parks in developing countries through a comparative analysis of Janmeda Park and Entoto Park in Addis Ababa, Ethiopia. The research assesses key dimensions, including accessibility, sociability, comfort, image projection, and management/maintenance. These elements are vital for understanding how well urban parks serve their communities and contribute to urban development. While Janmeda Park, despite its historical importance, struggles with physical and operational challenges, Entoto Park’s well-designed amenities, management, and safety provide a model for future park developments. The study offers recommendations to improve urban parks, such as enhancing accessibility, fostering sociability, ensuring user comfort and safety, strengthening image projection, integrating cultural and historical elements, and improving management and maintenance. It also highlights the importance of monitoring and evaluating park performance. These recommendations aim to guide urban planners and policymakers in enhancing urban parks, thereby supporting community well-being and promoting effective urban development. The insights provided are intended to inform better practices for urban park planning and management, contributing to more vibrant and functional green spaces in developing cities.

A domain adaptation framework for cross-modality SAR 3D reconstruction point clouds segmentation utilizing LiDAR data

Synthetic aperture radar (SAR) 3D point clouds reconstruction can eliminate the problems of layover in 2D SAR image projections, the recognition of the reconstructed point cloud can significantly enhance target identification and information extraction. Current SAR 3D point cloud segmentation methods, based on traditional machine learning clustering approaches, suffer from poor automation and accuracy. However, the absence of publicly labeled SAR datasets impedes the progress of deep learning-based SAR 3D point cloud segmentation methods. To tackle the aforementioned challenges, we introduce, for the first time, an alternative training approach for SAR 3D point cloud segmentation using LiDAR annotated data, which offers a more abundant sample pool, thus alleviating the lack of training sets. Nevertheless, a segmentation model trained on LiDAR point clouds exhibits a significant decline in performance when directly applied to SAR 3D reconstructed point clouds due to the cross-domain discrepancy. This research presents a pioneering domain adaptation 3D semantic segmentation framework to implement cross-modal learning for SAR point clouds. In our scheme, a simple yet effective technique is developed to achieve segmentation of SAR double-bounce scattering regions called SARDBS-Mix, which employs a mixing strategy, to capture the distinctive reflection characteristics of SAR data. Furthermore, we implement approaches including center alignment and normalization, local augmentation, and weighted cross entropy to mitigate LiDAR and SAR domain gap and class imbalances. The experimental results validate the feasibility and effectiveness of the proposed method for SAR 3D reconstructed point cloud segmentation.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model’s capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method’s effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Evaluation of micro-hologram activation for dynamic display applications

We explore the implementation of dynamic behavior in holographic displays through the activation of static elementary holograms. This research takes place in an ambitious concept of near-eye display, free of any focusing optical system. We investigate the dynamic addressing of holographic elements with a size of about 27 µm, distributed on centimeter-size samples. We project dynamic images with a resolution of 10 × 10 pixels. This first demonstration validates the ability to activate pixelated holograms with low cross talk, with an SNR of about 26. We project various images and analyze their rendering in an optical scheme consistent with visual behavior, using the integration time of a camera to mimic visual persistence. We demonstrate the image projection in a free space optical set-up using an SLM. This demonstration may pave the way to further potential developments implementing photonic integrated circuits inside the display.

The morphological discrepancy of neuromuscular junctions between bilateral paraspinal muscles in patients with adolescent idiopathic scoliosis: A quantitative immunofluorescence assay.

Prior studies suggested that neuromuscular factors might be involved in the pathogenesis of adolescent idiopathic scoliosis (AIS). The neuromuscular junction (NMJ) is the important pivot where the nervous system interacts with muscle fibers, but it has not been well characterized in the paraspinal muscles of AIS. This study aims to perform the quantitative morphological analysis of NMJs from paraspinal muscles of AIS. AIS patients who received surgery in our center were prospectively enrolled. Meanwhile, age-matched congenital scoliosis (CS) and non-scoliosis patients were also included as controls. Fresh samples of paraspinal muscles were harvested intraoperatively. NMJs were immunolabeled using different antibodies to reveal pre-synaptic neuronal architecture and post-synaptic motor endplates. A confocal microscope was used to acquire z-stack projections of NMJs images. Then, NMJs images were analyzed on maximum intensity projections using ImageJ software. The morphology of NMJs was quantitatively measured by a standardized 'NMJ-morph' workflow. A total of 21 variables were measured and compared between different groups. A total of 15 AIS patients, 10 CS patients and 5 normal controls were enrolled initially. For AIS group, NMJs in the convex side of paraspinal muscles demonstrated obviously decreased overlap when compared with the concave side (34.27% ± 8.09% vs. 48.11% ± 10.31%, p = 0.0036). However, no variables showed statistical difference between both sides of paraspinal muscles in CS patients. In contrast with non-scoliosis controls, both sides of paraspinal muscles in AIS patients demonstrated significantly smaller muscle bundle diameters. This study first elucidated the morphological features of NMJs from paraspinal muscles of AIS patients. The NMJs in the convex side showed smaller overlap for AIS patients, but no difference was found in CS. This proved further evidence that neuromuscular factors might contribute to the mechanisms of AIS and could be considered as a novel potential therapeutic target for the treatment of progressive AIS.

Digging into Depth and Color Spaces: A Mapping Constraint Network for Depth Super-Resolution

Scene depth super-resolution (DSR) poses an inherently ill-posed problem due to the extremely large space of one-to-many mapping functions from a given low-resolution (LR) depth map, which possesses limited depth information, to multiple plausible high-resolution (HR) depth maps. This characteristic renders the task highly challenging, as identifying an optimal solution becomes significantly intricate amidst this multitude of potential mappings. While simplistic constraints have been proposed to address the DSR task, the relationship between LR and HR depth maps and the color image has not been thoroughly investigated. In this paper, we introduce a novel mapping constraint network (MCNet) that incorporates additional constraints derived from both LR depth maps and color images. This integration aims to optimize the space of mapping functions and enhance the performance of DSR. Specifically, alongside the primary DSR network (DSRNet) dedicated to learning LR-to-HR mapping, we have developed an auxiliary degradation network (ADNet) that operates in reverse, generating the LR depth map from the reconstructed HR depth map to obtain depth features in LR space. To enhance the learning process of DSRNet in LR-to-HR mapping, we introduce two mapping constraints in LR space: 1) the cycle-consistent constraint, which offers additional supervision by establishing a closed loop between LR-to-HR and HR-to-LR mappings, and 2) the region-level contrastive constraint, aimed at reinforcing region-specific HR representations by explicitly modeling the consistency between LR and HR spaces. To leverage the color image effectively, we introduce a feature screening module (FSM) to adaptively fuse color features at different layers, which can simultaneously maintain strong structural context and suppress texture distraction through subspace generation and image projection. Comprehensive experimental results across synthetic and real-world benchmark datasets unequivocally demonstrate the superiority of our proposed method over state-of-the-art DSR methods. Our MCNet achieves an average MAD reduction of 3.7% and 7.5% over state-of-the-art DSR method for \(\times\) 8 and \(\times\) 16 cases on Milddleburry dataset, respectively, without incurring additional costs during inference.

Light from Afield: Fast, High-Resolution, and Layer-Free Deep Vat 3D Printing.

Harnessing light for cross-linking of photoresponsive materials has revolutionized the field of 3D printing. A wide variety of techniques leveraging broad-spectrum light shaping have been introduced as a way to achieve fast and high-resolution printing, with applications ranging from simple prototypes to biomimetic engineered tissues for regenerative medicine. Conventional light-based printing techniques use cross-linking of material in a layer-by-layer fashion to produce complex parts. Only recently, new techniques have emerged which deploy multidirection, tomographic, light-sheet or filamented light-based image projections deep into the volume of resin-filled vat for photoinitiation and cross-linking. These Deep Vat printing (DVP) approaches alleviate the need for layer-wise printing and enable unprecedented fabrication speeds (within a few seconds) with high resolution (>10 μm). Here, we elucidate the physics and chemistry of these processes, their commonalities and differences, as well as their emerging applications in biomedical and non-biomedical fields. Importantly, we highlight their limitations, and future scope of research that will improve the scalability and applicability of these DVP techniques in a wide variety of engineering and regenerative medicine applications.

3D scattering-enabled vectorial holographic projection with a binary amplitude hologram

Empowered by wavefront shaping (WFS) techniques, scattering materials (SMs) hold significant potential in high-capacity, high-fidelity, and crosstalk-free 3D holographic projections. Here, we present an optimal accumulation algorithm (OAA) to generate binary amplitude holograms that enable simultaneous control of 3D intensity and polarization distributions through SMs. In particular, OAA is efficient for creating binary holograms since only addition and comparison operations are required. Using such a binary hologram, we demonstrate complete polarization control on four planes simultaneously, and an average degree of polarization over 95% is achieved. Moreover, a 3D holographic projection of polarization-multiplexed images on multiple planes is also presented with an average Pearson correlation coefficient over 0.80. By exploiting the rapid switching ability of a digital micromirror device, we further demonstrate dynamic 3D vectorial holographic projections with reconfigurable binary amplitude holograms. Our proposed approach offers a competitive way to generate holograms for 3D scattering-enabled vectorial holographic projections.

Formation of a panoramic image of the inner surface of the pipe

Background. Visual non-destructive testing of the inner surface of pipes is an important aspect in their production and operation. A defect detected and corrected in a timely manner can significantly reduce the number of defects in production and prevent various emergency incidents during operation. Formation of a complete panoramic image of the inner surface of pipes suitable for quality analysis is an urgent and sought-after task that can be solved using computer vision systems. Aim. This work is the research and development of television methods for forming a complete panoramic image of the inner surface of a pipe, which can be analyzed to search for defects. Methods. To form a cylindrical panoramic image, mathematical models for the formation of an equidistant projection of spherical images obtained using a fisheye lens were used. For high-quality stitching of the resulting frames, digital image processing methods were used, including brightness and contrast transformations, and searching for special points using the MSER algorithm. Theoretical results are verified by full-scale modeling. Results. The result of this work is an algorithm for stitching frames of a video sequence generated by a television camera with a fisheye optical system, uniformly moved along the longitudinal axis of the pipe, into a single panoramic image of the internal surface. Conclusion. The algorithm ensures the formation of a high-quality image of a full panorama of the inner surface of the pipes with the absence of brightness artifacts.

Super-resolution Microscopy and Photo-lithography: How can one inspire the other

The principles of microscopy and lithography projection techniques are similar. To improve the resolution of the microscope or further reduce the image projection, both techniques face similar limitations, diffraction limits. The diffraction limit is a limitation of physical optics. In the development process of microscopes, to improve optical microscopy technology, researchers have proposed the idea of reducing the wavelength of light sources and increasing the numerical aperture based on the principle of diffraction limit generation. Modern scientists have proposed techniques to break through the diffraction limit and gradually increase the resolution of microscopes to further improve resolution. These breakthroughs also provide ideas for lithography technology, and researchers have invented techniques such as immersion lithography, multi-exposure, and two-photon direct writing. This article introduces the development process of super-resolution microscopy and lithography technology and analyzes the inspiration and influence of microscopy technology on lithography technology, that is, providing ideas for improving the accuracy of lithography technology.

3D Printing of Optical Lenses Assisted by Precision Spin Coating

AbstractThough 3D printing shows potential in fabricating complex optical components rapidly, its poor surface quality and dimensional accuracy render it unqualified for industrial optics applications. The layer steps in the building direction and the pixelated steps on each layer's contour result in inevitable microscale defects on the 3D‐printed surface, far away from the nanoscale roughness required for optics. This paper reports a customized vat photopolymerization‐based lens printing process, integrating unfocused image projection and precision spin coating to solve lateral and vertical stair‐stepping defects. A precision aspherical lens with less than 1 nm surface roughness and 1 µm profile accuracy is demonstrated. The 3D‐printed convex lens achieves a maximum MTF resolution of 347.7 lp mm−1. A mathematical model is established to predict and control the spin coating process on 3D‐printed surfaces precisely. Leveraging this low‐cost yet highly robust and repeatable 3D printing process, the precision fabrication of multi‐scale spherical, aspherical, and axicon lenses are showcased with sizes ranging from 3 to 70 mm using high clear photocuring resins. Additionally, molds are also printed to form multi‐scale PDMS‐based lenses.

On-line quantitative analysis of major elements in phosphate slurry using LIBS assisted by plasma information from orthogonal directions imaging

To solve the lag of chemical element information feedback and realize the on-line analysis of phosphorite flotation, LIBS was used to measure major elements in the phosphate slurry. Plasma images in two orthogonal directions were captured synchronously to describe the irregular plasma. The plasma image projections in different directions were calculated as the features. A spectral correction method with these features was proposed to solve the problems of large signal fluctuations and poor measurement accuracy. The proposed method was first applied in the filed application of phosphate slurry on-line analysis. The RSDs of the characteristic spectral intensity decreased from more than 20 % to less than 3 %, improving the stability of spectra significantly. The method was further combined with the calibration of the concentrations of P2O5, MgO and SiO2. The R2 and MAE of P2O5 were 0.99 and 0.5 %, respectively, meeting the accuracy requirements of online measurements.