Search Results (1,428)

Generative Adversarial Networks (GANs) have emerged as powerful tools for medical image synthesis and clinical outcome prediction. In ophthalmology, accurate forecasting of Optical Coherence Tomography (OCT) images and best-corrected visual acuity (BCVA) values can significantly enhance patient monitoring and personalized treatment planning. We introduce a multimodal GAN inspired by the StyleGAN architecture, featuring super-resolution modules, a multi-scale patch discriminator, and temporal attention mechanisms. To predict logMAR values, a hybrid deep–shallow LSTM model was jointly trained alongside the image pipeline. Synthesized scans were processed through an EfficientNet-based classifier to predict 16 retinal biomarkers. To ensure subject independence, we employed a 3-fold patient-level cross-validation strategy. The proposed multimodal GAN achieved an SSIM of 0.9264, an FID of 11.9, and a PSNR of 38.1 dB for OCT forecasting. The logMAR module delivered an MAE of 0.052, while the biomarker classifier attained a macro-F1 score of 0.81. Based on logMAR change forecasting, patients were further categorized into Winner, Stabilizer, and Loser outcome groups using a threshold of $\Delta =0.05$, achieving an overall F1 score of 0.84. Our approach effectively forecasts retinal morphology and functional outcomes, providing valuable predictive insights for proactive clinical decision-making in retinal health management. Full article

Background: Soft tissue stability around dental implant abutments is critical for maintaining a functional peri-implant seal. Yellow anodization is used to improve the aesthetic and surface characteristics of titanium abutments, yet its epithelial effects under more physiologically relevant 3D conditions remain insufficiently explored. Objective: To develop a 3D bioprinted in vitro peri-implant mucosa model and to compare epithelial cell responses on yellow anodized versus turned titanium abutment surfaces. Methods: Commercial Grade 5 (Ti6Al4V) titanium abutments were anodized and compared with turned controls. A collagen-based 3D bioprinted “collar-like” construct incorporating YD-38 epithelial cells was fabricated using a custom holder system to simulate peri-implant mucosal contact. Samples were cultured for 14 and 21 days. Cell distribution and morphology were assessed by optical microscopy and HE staining, while cytoskeletal organization was evaluated by TRITC-phalloidin/Hoechst staining and confocal microscopy. Quantitative fluorescence analysis was performed at 21 days. Results: Both surfaces supported epithelial coverage in the 3D environment. Anodized specimens showed more pronounced actin cytoskeletal organization and the presence of actin-rich, filamentous cellular extensions compared with turned controls. Quantitative image analysis demonstrated significantly higher TRITC-phalloidin signal intensity at 21 days on anodized samples (p < 0.001). Conclusions: Within the limitations of a 3D epithelial in vitro model using YD-38 cells, yellow anodization was associated with enhanced epithelial cytoskeletal organization compared with turned titanium. The presented 3D bioprinted platform may serve as a practical in vitro tool for screening abutment surface modifications relevant to peri-implant soft tissue integration. Full article

This study introduces a framework for estimating the optical scattering properties of thin-film phantoms using a custom-built Spectral-Domain Dental Optical Coherence Tomography (DEN-OCT) system operating within the 780–900 nm spectral range. The purpose of this work was to assess the performance of this system. The system exhibited high depth-resolved imaging performance with an axial resolution of approximately 16.30 µm, a signal-to-noise ratio of about 32.4 dB, and a 6 dB sensitivity roll-off depth near 2 mm, yielding an effective imaging range of 2.5 mm. Thin-film phantoms with controlled optical characteristics were fabricated and analyzed using Beer–Lambert and diffusion approximation models to evaluate attenuation behavior. Samples representing different tissue analogs demonstrated distinct scattering responses: one sample showed strong scattering similar to hard tissues, while the others exhibited lower scattering and higher transmission, resembling soft-tissue properties. Spectrophotometric measurements at 840 nm supported these trends through characteristic transmittance and reflectance profiles. While homogeneous samples conformed to analytical models, the highly scattering sample deviated due to structural non-uniformity, requiring Monte Carlo simulation to accurately describe photon transport. OCT A-scan analyses fitted with exponential decay models produced attenuation coefficients consistent with spectrophotometric data, confirming the dominance of scattering over absorption. The integration of OCT imaging, optical modeling, and Monte Carlo simulation establishes a reliable methodology for quantitative scattering estimation and demonstrates the potential of the developed DEN-OCT system for advanced dental and biomedical imaging applications. The innovation of this work lies in the integration of phantom-based optical calibration, multi-model scattering analysis, and depth-resolved OCT signal modeling, providing a validated pathway for quantitative parameter extraction in dental OCT applications. Full article

An ongoing challenge in pelagic oceanography and limnology is to quantify and understand the distribution of suspended particles and particle aggregates with sufficient temporal and spatial fidelity to understand their dynamics. These particles include biotic (mesoplankton, organic fragments, fecal pellets, etc.) and abiotic (dusts, precipitates, sediments and flocks, anthropogenic materials, etc.) matter and their aggregates (i.e., marine snow), which form a large part of the total particulate matter > 200 μm in size in the ocean. The transport of organic material from surface waters to the deep-sea floor is of particular interest, as it is recognized as a key factor controlling the global carbon cycle and hence, a critical process influencing the sequestration of carbon dioxide from the atmosphere. Here we describe the development of an oceanographic instrument, the Pelagic Laser Tomographer (PLT), that uses high-resolution optical technology, coupled with post-processing analysis, to scan the 3D content of the water column to detect and quantify 3D distributions of small particles. Existing optical instruments typically trade sampling volume for spatial resolution or require large, complex platforms. The PLT addresses this gap by combining high-resolution laser-sheet imaging with large effective sampling volumes in a compact, deployable system. The PLT can generate spatial distributions of small particles (~100 µm and larger) across large water volumes (order 100–1000 m³) during a typical deployment, and allow measurements of particle patchiness over spatial scales to less than 1 mm. The instrument’s small size (6 kg), high resolution (~100 µm in each 3000 cm² tomographic image slice), and analysis software provide a tool for pelagic studies that have typically been limited by high cost, data storage, resolution, and mechanical constraints, all usually necessitating bulky instrumentation and infrequent deployment, typically requiring a large research vessel. Full article

Background: Orbital dermoid cysts are common benign lesions; however, deep-seated or recurrent lesions near the orbital apex pose major surgical challenges due to their proximity to critical neurovascular structures. Lateral orbitotomy remains the reference approach, but accurate osteotomies and stable reconstruction can be difficult to achieve using conventional techniques. This study reports our initial experience using a fully digital, hospital-based point-of-care (POC) workflow to enhance precision and safety in complex orbital dermoid cyst surgery. Methods: We present a case series of three patients with orbital dermoid cysts treated at a tertiary center (2024–2025) using a comprehensive digital workflow. Preoperative assessment included CT and/or MRI followed by virtual surgical planning (VSP) with orbit–tumor segmentation and 3D modeling. Cutting guides and patient-specific implants (PSIs) were manufactured in-house under a certified hospital-based POC protocol. Surgical strategies were tailored to each lesion and included piezoelectric osteotomy, intraoperative navigation, intraoperative CT, and structured-light scanning when indicated. Results: Complete en bloc resection was achieved in all cases without capsular rupture or optic nerve injury. Intraoperative CT confirmed complete lesion removal and accurate PSI positioning and fitting. Structured-light scanning enabled radiation-free postoperative monitoring when used. All patients preserved full ocular motility, visual acuity, and facial symmetry, with no complications or recurrences during follow-up. Conclusions: The integration of VSP, in-house POC manufacturing, and image-guided surgery within a lateral orbitotomy approach provides a reproducible and fully integrated workflow. This strategy appears to improve surgical precision and safety while supporting optimal long-term functional and aesthetic outcomes in challenging orbital dermoid cyst cases. Full article

Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation. Recently, several studies have explored the use of optical neural networks represented by the diffractive deep neural network (D²NN) for GANs. However, most of these focus on applications of the generative network, and there is currently no well-established D²NN architecture that simultaneously implements generative adversarial functionality. Here, we propose a novel implementation scheme for generative adversarial networks based on all-optical diffraction layers, demonstrating a complete all-optical adversarial architecture that simultaneously realizes both the generative network and the adversarial network (D²NN-GAN). We validated this method on the MNIST handwritten digit dataset, achieving Nash equilibrium convergence with the discriminator accuracy stabilizing around 50%. Concurrently, the average SSIM parameter of generated images reached 0.9573, indicating that the generated samples possess high quality and closely resemble real samples. Furthermore, we extended the framework to the KTH human action dataset, successfully reconstructing the “running” action with a discriminator accuracy of approximately 75%. The D²NN-GAN architecture introduces a fully optical generative adversarial model, providing a practical path for future optical modeling methods, such as image generation and video synthesis. Full article

Background/Objectives: To evaluate the predictive accuracy of intraocular lens (IOL) power calculation by ray tracing in eyes with previous radial keratotomy (RK). Methods: A consecutive series of eyes with previous RK was retrospectively analyzed. Preoperatively, all eyes underwent optical biometry to measure the axial length (AL) and anterior segment imaging by the MS-39 (CSO), which combines Placido disk corneal topography and anterior segment optical coherence tomography. The built-in ray tracing software was used to calculate the IOL power. For comparative purposes, the results of the Barrett True-K, EVO, Haigis total keratometry, and PEARL-DGS formulas were also investigated. The refractive outcomes were evaluated with Eyetemis. Results: Twenty-four eyes (24 patients) were investigated. The mean AL and keratometry were, respectively, 27.34 ± 2.88 mm and 35.53 ± 3.66 diopters (D). The mean prediction error (PE) was −0.03 ± 0.65 D (range: from −1.30 to +1.64 D). The mean and median absolute errors were 0.52 and 0.48 D, respectively. The percentages of eyes with a PE within ±0.25 D, ±0.50 D, and ±1.00 D were 29.17%, 62.50%, and 87.50%, respectively. A comparison with the other formulas was possible in 20 eyes and did not reveal any statistically significant differences; the percentage of eyes with a PE within ±0.50 D ranged from 50 to 65%. Conclusions: Ray tracing is a relatively accurate solution for calculating the IOL power in eyes with previous RK. Paraxial formulas provide similar outcomes and should be considered in these patients. The refractive outcomes of IOL power calculation in post-RK eyes are still below modern benchmarks for virgin eyes. Full article

Intraoperative assessment of pancreatic quality, followed by sampling for the potential isolation of Langerhans islets for subsequent autotransplantation, is currently a key component of post-total pancreatectomy diabetes mellitus treatment. The aim of this study was to quantitatively evaluate pancreatic parenchymal stiffness using optical coherence elastography (OCE) imaging, and to investigate the utility of the OCE method as a potential indicator of islet yield after pancreatectomy. A total of 41 freshly excised human pancreatic specimens, containing pancreatic ductal adenocarcinoma (PDAC) and surrounding non-tumorous tissues post-pancreatectomy, were studied. In this research, the stiffness (Young’s modulus, kPa) and its color-coded 2D distribution were calculated for various pancreatic samples using compression OCE. Stiffness values were compared between intact pancreatic parenchyma (islet-poor and islet-rich) and pancreatic lesion groups (parenchymal fibrosis and/or PDAC invasion). The data were confirmed by histological analysis. In addition, the measured stiffness values for various morphological groups of the pancreatic samples were compared with the number of isolated islets obtained from pancreatic samples after collagenase treatment. The study demonstrated that OCE can effectively distinguish areas of pancreatic lesions and identify intact pancreatic parenchyma containing Langerhans islets. A highly significant increase in mean stiffness (p < 0.0001) was observed in postoperative pancreatic samples exhibiting signs of parenchymal fibrosis or PDAC invasion compared to unaffected, intact pancreatic parenchyma. For the first time, a relationship between stiffness values and the number of isolated pancreatic islets was demonstrated; in particular, the number of isolated islets significantly decreased (≤110 pcs/g) in samples exhibiting stiffness values above 150 kPa and below 75 kPa. The optimal stiffness range for the efficient isolation of islets (≥120 pcs/g) from pancreatic tissue was identified as 75–150 kPa. The study introduces a novel approach for rapid and objective intraoperative assessment of pancreatic tissue quality using real-time OCE data. This technique facilitates the identification of regions affected by pancreatic lesions and supports the selection of intact pancreatic parenchyma, potentially enhancing the accuracy of Langerhans islet yield predictions during surgical resection. Full article

Early-age shrinkage in 3D-printed concrete constitutes a critical applied challenge due to the rapid development of deformations and the absence of conventional reinforcement systems. From a scientific standpoint, a clear knowledge gap exists in materials science concerning the reliable quantification of very small, rapidly evolving strains in fresh and early-age cementitious materials produced by additive manufacturing. This study investigates practical and low-cost alternatives to commercial optical systems for monitoring early-age shrinkage in 3D-printed concrete, a key challenge given the rapid deformation of printed elements and their typical lack of reinforcement. The work focuses on identifying both the most precise method for capturing minor, fast-developing strains and affordable tools suitable for laboratories without access to advanced equipment. Three mixtures with different aggregate types were examined to broaden the applicability of the findings and to evaluate how aggregate selection affects fresh properties, hardened performance, and shrinkage behavior. Shrinkage measurements were carried out using a commercial digital image correlation system, which served as the reference method, along with simplified optical setups based on a smartphone camera and a GoPro device. Additional measurements were performed with laser displacement sensors and Linear Variable Differential Transformer LVDT transducers mounted in a dedicated fixture. Results were compared with the standardized linear shrinkage test to assess precision, stability, and the influence of curing conditions. The findings show that early-age shrinkage must be monitored immediately after printing and under controlled environmental conditions. When the results obtained after 12 h of measurement were compared with the values recorded using the commercial reference system, differences of 19%, 13%, 16%, and 14% were observed for the smartphone-based method, the GoPro system, the laser sensors, and the LVDT transducers, respectively. Full article

Underwater optical images are the primary carriers of underwater scene information, playing a crucial role in marine resource exploration, underwater environmental monitoring, and engineering inspection. However, wavelength-dependent absorption and scattering severely deteriorate underwater images, leading to reduced contrast, chromatic distortions, and loss of structural details. To address these issues, we propose a U-shaped underwater image enhancement framework that integrates Swin-Transformer blocks with lightweight attention and residual modules. A Dual-Window Multi-Head Self-Attention (DWMSA) in the bottleneck models long-range context while preserving fine local structure. A Global-Aware Attention Map (GAMP) adaptively re-weights channels and spatial locations to focus on severely degraded regions. A Feature-Augmentation Residual Network (FARN) stabilizes deep training and emphasizes texture and color fidelity. Trained with a combination of Charbonnier, perceptual, and edge losses, our method achieves state-of-the-art results in PSNR and SSIM, the lowest LPIPS, and improvements in UIQM and UCIQE on the UFO-120 and EUVP datasets, with average metrics of PSNR 29.5 dB, SSIM 0.94, LPIPS 0.17, UIQM 3.62, and UCIQE 0.59. Qualitative results show reduced color cast, restored contrast, and sharper details. Code, weights, and evaluation scripts will be released to support reproducibility. Full article

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology. Full article

Objectives: This study aimed to investigate the association between optical coherence tomography angiography (OCTA) parameters and cardiovascular (CV) risk scores in individuals with type 1 diabetes (T1D). Methods: A cross-sectional analysis of a large-scale prospective OCTA trial cohort (ClinicalTrials.gov NCT03422965) was performed. Demographic, systemic, and ocular data—including OCTA imaging—were collected. T1D participants were stratified into three CV risk categories: moderate (MR), high (HR), and very high risk (VHR). Individualized predictions for fatal and non-fatal CV events at 5 and 10 years were calculated using the STENO T1 Risk Engine calculator. Results: A total of 501 individuals (1 eye/patient; 397 T1D, 104 controls) were included. Subjects with MR (n = 37), HR (n = 152) and VHR (n = 208) exhibited significantly reduced vessel density (VD) (20.9 ± 1.3 vs. 20.2 ± 1.6 vs. 19.3 ± 1.8 mm⁻¹, p < 0.05), perfusion density (PD) (0.37 ± 0.02 vs. 0.36 ± 0.02 vs. 0.35 ± 0.02%, p < 0.05) and foveal avascular zone circularity (0.69 ± 0.06 vs. 0.65 ± 0.07 vs. 0.63 ± 0.09, p < 0.05). Statistically significant negative correlations were observed between CV risk and OCTA parameters including VD, PD, and retinal nerve fiber layer thickness, while central macular thickness (CMT) showed a positive correlation (p < 0.05). Notably, CMT was significantly associated with 5-year CV risk. Conclusions: OCTA-derived metrics, particularly reduced retinal VD and PD, are associated with elevated CV risk scores in T1D patients. These findings suggest that OCTA may serve as a valuable non-invasive tool for identifying individuals with increased CV risk scores. Full article

Images captured underwater frequently suffer from color casts, blurring, and distortion, which are mainly attributable to the unique optical characteristics of water. Although conventional UIE methods rooted in physics are available, their effectiveness is often constrained, particularly in challenging aquatic and illumination conditions. More recently, deep learning has become a leading paradigm for UIE, recognized for its superior performance and operational efficiency. This paper proposes UCA-Net, a lightweight CNN-Transformer hybrid network. It incorporates multiple attention mechanisms and utilizes composite attention to effectively enhance textures, reduce blur, and correct color. A novel adaptive sparse self-attention module is introduced to jointly restore global color consistency and fine local details. The model employs a U-shaped encoder–decoder architecture with three-stage up- and down-sampling, facilitating multi-scale feature extraction and global context fusion for high-quality enhancement. Experimental results on multiple public datasets demonstrate UCA-Net’s superior performance, achieving a PSNR of 24.75 dB and an SSIM of 0.89 on the UIEB dataset, while maintaining an extremely low computational cost with only 1.44M parameters. Its effectiveness is further validated by improvements in various downstream image tasks. UCA-Net achieves an optimal balance between performance and efficiency, offering a robust and practical solution for underwater vision applications. Full article

This study analyzed the spatial distribution of large green algae (LGA) in the northern coastal waters of China, including the Yellow Sea and Bohai Sea, using Coastal Zone Imager (CZI) data from the HY-1C/D satellites. An inversion model (coastal zone imager model) of LGA was established, based on which the distribution details of large green algae in the Yellow Sea and Bohai Sea were investigated. The results indicated the following: (1) LGA exhibits a clearly seasonal pattern from May to August. Initially occurrences are detected in May in the southern Yellow Sea (32–34° N), followed by a rapid expansion and intensification from June to mid-July, with peak distribution around 35° N near the Shandong Peninsula. The affected area subsequently decreases in late August. (2) High LGA coverage is mainly concentrated along the Subei Shoal and the Shandong Peninsula in the Yellow Sea, as well as the coastal regions of Yantai, Qinhuangdao, and Yingkou in the Bohai Sea. (3) The LGA-M inversion model demonstrates stable performance in nearshore waters with similar optical characteristics and is applicable to LGA extraction in adjacent coastal seas, highlighting the potential of HY-1C/D satellite data in marine environmental monitoring and protection. Full article

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies. Full article