DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) has become a widely adopted single-molecule localization microscopy (SMLM) technique owing to its high spatial resolution, versatile labeling strategies, and theoretically unlimited multiplexing capability. Recent developments in repetitive docking strand designs have enabled faster image acquisition by increasing the number of potential binding motifs per target. However, the effect of such architectural modifications on effective spatial resolution remains largely unexplored. Here, we systematically quantify how repetitive docking strands influence localization distributions and effective resolution using the well-defined geometry of the trimeric proliferating cell nuclear antigen (PCNA) as a model system. Whereas classical single-motif docking strands resolve the expected ∼6 nm spacing between PCNA subunits with high precision, repetitive docking motifs produce broadened localization distributions, despite comparable localization precision. Our results suggest that spatial blurring arises from a combination of variable binding site geometry, rotational flexibility of elongated multivalent DNA docking sequences, as well as the dynamic behavior of imager strands. This study provides a quantitative framework for understanding how docking strand architecture determines resolution limits in DNA-PAINT and underscores the need to balance multiplexing and imaging speed with structural fidelity. Our results thus offer guidance for the rational design of docking strands for high-precision DNA-PAINT imaging of protein complexes.

As key load-bearing elements, bridge piers are highly sensitive to concrete construction quality, which directly affects structural safety and durability. Surface defects such as color differences and bugholes are common and, if not properly assessed, may accelerate deterioration. To overcome the limitations of conventional inspection methods in precision, efficiency, and objectivity, this study proposes an intelligent framework integrating high-resolution panoramic imaging, deep semantic segmentation, and spatial color-difference analysis. The main contributions include: (1) A segment-based matrix image acquisition strategy is developed using unmanned aerial vehicles, combined with a DSeam-driven deep stitching algorithm to generate panoramic images of piers with geometric consistency and luminance continuity, serving as a unified visual basis for defect recognition. (2) For bughole segmentation, a PoreMamba-Net semantic network based on a state space modeling architecture is constructed. The network employs multi-branch convolution and selective scanning mechanisms to enhance fine-grained perception and contextual representation of small targets, enabling high-precision segmentation. A quantitative evaluation system composed of the apparent bughole area ratio, maximum bughole diameter, and bughole distribution standard deviation is further proposed to comprehensively characterize bughole quantity, size, and spatial uniformity. (3) For unbounded and non-structural color difference defects, a pixel-level analysis method is proposed by combining multi-scale retinex with color restoration-based illumination normalization and the CIE94 color difference model. Moran’s I spatial autocorrelation index is introduced to jointly quantify color deviation intensity and spatial consistency, supporting classification of color differences and detection of clustered anomalies. The proposed framework is validated on typical piers from the Shiziyang Channel Bridge construction project in Guangdong Province. Results demonstrate strong adaptability, robustness, and accuracy in complex field conditions, providing reliable technical support for concrete quality acceptance and lifecycle management.

To compare image quality, agreement in left atrial fibrosis (LAF) quantification, and acquisition time for late gadolinium enhancement (LGE) imaging between three-dimensional whole-heart modified Dixon (mDixon) and phase-sensitive inversion recovery (PSIR) sequences of cardiac magnetic resonance (CMR) in patients with atrial fibrillation (AF). Thirty patients with AF who underwent LGE-CMR were prospectively enrolled. Subjective image quality scores, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), and acquisition time were compared, and inter-sequence agreement in LAF quantification was assessed. Compared with PSIR-LGE, mDixon-LGE achieved a higher rate of excellent image quality (80% vs. 60%, P = 0.031), higher CNRLGE-blood pool (41.50 ± 21.92 vs. 18.10 ± 6.65, P < 0.001), and higher CRLGE-blood pool (0.22 ± 0.11 vs. 0.11 ± 0.04, P < 0.001). Bland-Altman analysis demonstrated good agreement between mDixon-LGE and PSIR-LGE for total LAF (bias: - 0.36%; 95% LoA: - 10.58% to 9.84%), interstitial LAF (bias: 0.15%; 95% LoA: -6.20% to 6.50%), and dense LAF (bias: -0.52%; 95% LoA: -6.67% to 5.62%). The mDixon-LGE acquisition time (6.7 ± 2.4min) was significantly shorter than that of PSIR-LGE (9.0 ± 2.7min, P < 0.001). mDixon-LGE and PSIR-LGE demonstrated good agreement in quantifying LAF in patients with AF. mDixon-LGE showed a higher proportion of excellent image quality ratings, higher measured LGE-to-blood pool contrast values, and shorter acquisition time, supporting its use as a technically feasible and time-efficient alternative for LAF assessment.

Telepathology has emerged as a transformative digital health solution to address the global shortage of pathologists and the unequal distribution of diagnostic services, particularly in underserved and rural areas. In Henan Province, China, high diagnostic demand, rapid population growth, and limited pathology expertise exacerbate regional health care inequities, leading to delayed diagnoses and restricted access to specialist care. This study aimed to design, implement, and evaluate a province-wide telepathology system integrating web and mobile platforms to enhance diagnostic quality, efficiency, and equitable access across health care tiers. We conducted a retrospective, multicenter observational study using deidentified data from 120 health care institutions between 2016 and 2024. The system used a 3-tier architecture with virtual private network-secured transmission and a Browser-Server framework, supporting standardized whole-slide image acquisition, remote review, and reporting via web interfaces and a WeChat (Tencent) mini-program. System performance was assessed by consultation volume, turnaround time, concurrency, and diagnostic concordance in a subset of 1027 cases with paired tertiary-hospital expert diagnoses. Economic impact was estimated using previously published per-case savings, reflecting patient travel and ancillary cost reductions. Additional assessments included workflow integration, mobile platform use, and system stability under peak load. Over 8 years, the network processed 72,916 consultations encompassing 355,104 whole-slide images, supporting 220-300 concurrent users with stable performance. Median turnaround time was 10.06 (IQR 1.63-29.10) hours, with 96.41% (70,298/72,916) of cases completed within 72 hours. County-level hospitals contributed 77.63% (56,603/72,916) of consultations, demonstrating substantial engagement from lower-tier institutions. In the diagnostic subset, originating-site preliminary classifications achieved 0.90 sensitivity and 0.75 specificity relative to expert reference diagnoses, with 17.2% discordance corrected through remote expert review. Estimated annual direct cost savings ranged from US $0.14 to $0.63 million. Mobile-enabled access facilitated remote review and reporting without compromising data security, supporting integration into routine clinical workflows across diverse hospital settings. The Henan Province telepathology system demonstrates that a centrally coordinated, scalable digital health platform can improve diagnostic efficiency, quality, and equity in resource-constrained settings. High county-level hospital use highlights its potential to reduce geographic and structural diagnostic inequities. Future work should explore formal cost-effectiveness evaluation, artificial intelligence-assisted diagnostic support, and cross-regional interoperability to enable broader adoption and sustainable integration into health care systems.

The IVIS imaging system: Principle, performance, applications and perspectives in postharvest research.

Ex vivo digital microscopy uses light in the visible and adjacent spectra to obtain digital images of tissues. They are optical imaging techniques that allow the acquisition of digital images of tissues with minimal or no tissue preparation and are currently available for evaluation of fresh and/or fixed tissues. This review will provide an overview of the different types of ex vivo digital microscopy techniques, including confocal microscopy (CM), optical coherence tomography (OCT), stimulated Raman Spectroscopy (SRS), light sheet microscopy (LSM), microscopy with ultraviolet excitation (MUSE), structured illumination microscopy (SIM), and nonlinear microscopy (NLM). Except for OCT and SRS, all the other tissue imaging techniques require labeling of tissues with fluorescent dyes to obtain digital images. An advantage of several of these techniques, including fluorescence CM, SRS, LSM, MUSE, SIM, and NLM, is that they can produce hematoxylin and eosin-like images. The promising potential of ex vivo digital microscopy techniques in surgical pathology practice is supported by several retrospective and limited prospective studies. Applications of ex vivo digital microscopy techniques include real-time evaluation of fresh tissue at the bedside in clinics and radiology suites, as well as intraoperatively in pathology laboratories. These techniques have great potential for incorporation into standard-of-care surgical pathology practice.

Micro-defects, such as ink spots, scratches, and sintering formed during the screen printing process of photovoltaic cells, significantly impair module performance. Traditional machine vision methods exhibit limited detection efficiency and high false-positive and missed-detection rates, while existing deep learning algorithms struggle to achieve accurate and adaptive detection of small-target defects and background similar defects in complex industrial environments. This study proposes an enhanced defect detection methodology based on an improved YOLOv8 algorithm. A multi-focus image acquisition platform using primary and auxiliary CCDs was independently developed, integrating a high-frame-rate industrial camera and a high-resolution electron microscope, with an LED ring light employed to suppress reflections, thereby establishing a high-quality dataset covering three defect categories. The algorithm was optimized through multiple dimensions: the RepNCSPELAN4 module was incorporated into the backbone network to improve multi-scale feature fusion, and a novel wavelet transform-based WaveConv module was designed to replace traditional downsampling, thereby better preserving defect edges and texture details. The neck network integrates a lightweight shuffle attention mechanism and a new detail enhancement module to strengthen critical features while controlling model complexity. Additionally, a dedicated auxiliary detection head was added for spotting tiny ink dots. Experimental results demonstrate a marked improvement in performance: on the custom dataset, the improved model achieves a stable mean average precision of approximately 92%. Specifically, ink spot detection reached a precision of 84.9% and recall of 77.7%, effectively reducing missed small-target defects; sintering defect detection attained 98.9% precision and 100% recall, addressing previous misclassifications due to background similarity; and scratch detection precision improved to 92.2%. Visual comparisons confirm that the enhanced model effectively overcomes the limitations of the original approach. By constructing a specialized dataset and implementing targeted, coordinated optimizations to the YOLOv8 architecture, this study significantly enhances the accuracy and robustness of screen-printing defect detection in photovoltaic cells, providing an effective solution for real-time online quality inspection in smart manufacturing lines.

Knee injuries are one of the most common complaints in sports medicine. Magnetic resonance imaging is an essential adjunct to clinical evaluation for many traumatic injuries and overuse conditions. Given the heavy use of knee magnetic resonance imaging, developing faster magnetic resonance imaging acquisition methods and deployment in clinical practice would be valuable. In this article, we illustrate a spectrum of knee abnormalities from our clinical practice, utilizing a recently developed, publicly available sub-5-minute knee magnetic resonance imaging protocol with super-resolution image reconstruction based on deep learning. We review common traumatic injuries and overuse conditions of the knee and illustrate cases with this novel fast knee magnetic resonance imaging protocol.

Light-field microscopy (LFM) is a scanning-free 3D imaging technique that is useful for observing dynamic biological systems due to its unique capability to capture both spatial and angular information from samples in a single exposure. However, LFM suffers from the spatial-angular information trade-off associated with microlens arrays, and its spatial resolution is usually unsatisfactory for fine-structure imaging. To overcome this bottleneck, we introduce a deep-learning-based image fusion technique that combines LFM images with Fourier LFM (FLFM) images. The high spatial resolution of FLFM is combined with the dense angular acquisition capability of LFM to improve 3D image reconstruction quality. The deep learning network was trained with LFM, FLFM, and epipolar plane image data. The proposed neural network employs specialized feature extraction modules for each modality, with a U-Net backbone for 3D reconstruction, and integrates a hierarchical cascade-based result-level fusion strategy to jointly optimize multimodal features. This approach significantly enhances detail preservation and depth recovery in the final output. Results obtained using a publicly available dataset of synthetic tubulins demonstrate that the proposed method outperforms state-of-the-art techniques. Quantitatively, it achieved a peak signal-to-noise ratio (PSNR) of 38.4729 and a structural similarity index measure (SSIM) of 0.9876, significantly outperforming both traditional algorithms and single-modality deep learning approaches. Furthermore, validation on a mouse brain blood vessels dataset confirms the effectiveness of the method in reconstructing biological structures, achieving a PSNR of 35.0548 and an SSIM of 0.8424. We introduce an approach that combines LFM with FLFM, providing an efficient and reliable solution for practical LFM applications. The deep-learning-based framework demonstrates significant potential to simultaneously accelerate imaging acquisition and enhance 3D reconstruction quality, offering further possibilities for computational microscopy.

Three-dimensional (3D) microscopic imaging techniques are facing challenges to achieve greater imaging depth and enhanced visualization of results. In this paper, we propose a 3D microscope acquisition method based on a liquid lens with the translational field of view (LLTFOV). The method utilises the fast zoom function of the LLTFOV to achieve rapid depth focusing of the microscope, obtain parallax images of the specimen with complete depth information, and improve the accuracy and reliability of 3D reconstruction. By utilizing the LLTFOV's precise field of view (FOV) adjustment function, the parallax image of the specimen with multi-angle, uniform, and no vertical parallax is obtained, and the vizualisation effect of the 3D image of the specimen is improved. The 3D image of the specimen is obtained by displaying the acquired parallax synthesis image using a 3D display screen. The experimental results demonstrate that the method can efficiently reproduce the 3D images of the specimens, improving the image quality and visual experience, making it easier to obtain high-quality 3D images in scientific research and applications.

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present.

Artificial intelligence (AI) is increasingly integrated into point-of-care ultrasound (POCUS) to enhance its utility in critical care settings. This manuscript explores the current state of AI applications in POCUS, focusing on key domains such as image acquisition, image interpretation, education, task automation, procedural guidance, program development, and quality assurance. AI-driven tools can potentially improve image quality, provide real-time feedback, and assist in the interpretation of ultrasound images, thereby democratizing the use of POCUS across varying levels of operator expertise. This narrative review highlights relevant studies demonstrating the clinical utility of AI in POCUS, discusses the challenges that remain, and provides insights into future developments. The goal is to equip intensivists with a comprehensive understanding of how AI can support POCUS practice today and what advancements are on the horizon.

Optimizing contrast enhanced mammography: A comprehensive review of artefacts, causes, and remedies.

Development and validation of an interface for automated image acquisition during high-temperature environmental scanning electron microscopy experiments.

Visualization Tools in Radiology: A RRA Perspective on Virtual Reality, Augmented Reality, and 3D Printing.

Whole-body (WB) diffusion-weighted (DW) (WB-DW) MRI and fluorine 18-fluorodeoxyglucose (FDG) PET/CT have increasing roles in the evaluation of treatment response in the setting of multiple myeloma (MM), complementing molecular and clinical criteria. The authors clarify the use of WB-DW MRI and FDG PET/CT in the follow-up of MM, emphasize the advantages and limitations of these examinations, and outline the use of the response criteria for each modality. The Myeloma Response Assessment and Diagnosis System is a consensus guideline that provides recommendations for the acquisition and interpretation of WB-DW MR images of MM for diagnosis and follow-up. It is used to categorize treatment responses into five response assessment categories (RACs): RAC 1 (highly likely to be responding), RAC 2 (likely to be responding), RAC 3 (no change), RAC 4 (likely to be progressing), and RAC 5 (highly likely to be progressing). Conversely, the Italian Myeloma Criteria for PET Use is used to standardize FDG PET/CT image interpretation. The five-point Deauville score (DS) scale is used to grade FDG uptake visually according to metabolic response: Complete response is defined as uptake lower than that in the liver (DS 1-3); partial response, as persistent lesions, with a decreased number of lesions or reduced uptake (DS 4-5 ); stable disease, as no change; and progressive disease, as new lesions or increased uptake. WB-DW MRI and FDG PET/CT provide valuable information for posttreatment MM assessment. FDG PET/CT is the reference standard for detecting extramedullary disease, while WB-DW MRI is more sensitive, particularly for diffuse disease. For comprehensive evaluation, the morphologic changes in MM lesions should be correlated with DW imaging and apparent diffusion coefficient values at MRI and with uptake values at FDG PET/CT. ©RSNA, 2026 Supplemental material is available for this article.

To address the issue of suboptimal flare elimination using a fixed polarization angle in production workshops—due to complex workpiece placement, varying light source intensities, and incident light angles—this study proposes applying image grayscale histogram analysis. By comparing the changes in pixel counts corresponding to white flares across grayscale histograms at different polarization angles, the effectiveness of flare elimination is determined. This approach identifies the optimal polarization angle to reduce or eliminate white flare regions. First, a polarization model for surface reflection and a polarization-based glare elimination model are established to demonstrate the feasibility of using polarization principles for glare reduction. Then, combined with grayscale feature analysis, an optimization method for determining the polarization angle is derived. Experimental results demonstrate that the grayscale histogram effectively reflects brightness distribution changes in flare regions, showcasing the polarization filtering effect and providing reliable criteria for determining the optimal polarization angle. This method is easy to implement and suitable for optimizing image acquisition under complex lighting conditions.

Background/Objectives: We aimed to evaluate the diagnostic accuracy of the MONA.health artificial intelligence (AI) software (Version 1.0.0; MONA.health, Leuven, Belgium) and compare its advantages in screening for diabetic retinopathy (DR) and diabetic macular edema (DME) with standard fundus photography. Methods: This cross-sectional, real-life instrument validation study was conducted at the Vuk Vrhovac University Clinic in Zagreb during routine DR screening and included 296 patients (592 eyes) with diabetes. Following standard fundus photography using a 45° Zeiss VISUCAM NM/FA camera (Carl Zeiss Meditec AG, Jena, Germany), each patient also underwent imaging with an automated portable retinal camera (NFC-600, Crystalvue Ophthalmic Instruments, Taoyuan City, Taiwan). Two retina specialists independently graded images from the standard camera, while images from the NFC-600 were analyzed using the MONA.health AI software. Results: Among the 592 eyes, human grading identified 81 with any DR, including 17 with mild NPDR, 64 with referable DR (moderate/severe NPDR or PDR), and 13 with DME. The MONA.health AI software identified 65 eyes with referable DR and 19 with DME. For MONA DR screening compared to the standard fundus camera, the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa agreement, diagnostic odds ratio, and diagnostic effectiveness were 99.74%, 100%, 99.81%, 99.33%, 100%, 528.00, 0.00, 0.99, infinity, and 99.85%, respectively. For MONA DME screening, these metrics were 97.97%, 100%, 98.95%, 85.93%, 100%, 95.67, 0.00, 0.81, infinity, and 99.02%, respectively. The MONA AI screening process required 1 day of training and approximately 5 min for image capture and analysis, compared to 7 days of training and 13 min for image acquisition and grading with the standard method. Conclusions: These findings demonstrate that the MONA.health AI software matches the accuracy of standard fundus photography for screening and early detection of referable DR and DME, while offering a faster, simpler, and more user-friendly workflow that significantly reduces the time to obtain screening results.

Protocol to study adult neurogenesis in fresh-frozen human hippocampal tissue using an immunofluorescence quantitative approach.

Abstract Reservoirs have spatial variation in water depth, suspended matter, and biogeochemistry that can influence patterns of water clarity and color. Spatial surveys with sensor‐equipped boats matched in time with satellite image acquisition provide data‐intensive avenues for understanding spatial patterns of optical properties within reservoirs. We combined continuous field data from high‐speed spatial surveys with Sentinel‐2 imagery to map water clarity and color in six subtropical Texas reservoirs during summer. Reservoir arms had lower clarity and longer dominant wavelengths than the main bodies, indicating higher concentrations of optical constituents. Because surveys took place during a period of low inflows associated with drought, color and clarity patterns may have been associated mainly with phytoplankton communities and resuspension of shallow sediments, rather than direct river inputs. Whole system analysis of dominant wavelength showed that five reservoirs reflected green (550–569 nm) over most of their surface area, suggesting high concentrations of phytoplankton biomass, and one reservoir in a clay‐dominated watershed was primarily yellow to brown (570–583 nm). Water clarity spanned a wide turbidity range (3.18–95.2 NTU) and was low over most of the surface area of these systems (&lt;1.5 m Secchi). In five bio‐optical models of turbidity, the best model performance occurred between 0 and 50 NTU, with unexplained variation at higher turbidity possibly linked to optically distinct classes of suspended sediment, phytoplankton pigmentation, or dissolved organic matter. These results indicate that reservoirs can have considerable spatial heterogeneity in water clarity and color, setting the stage for future spatial snapshots that encompass interannual and seasonal variability in precipitation.