High-resolution Imaging Research Articles

Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease

Stargardt disease type 1 (STGD1) is a genetic disorder that leads to progressive vision loss, with no approved treatments currently available. The development of effective therapies faces the challenge of identifying appropriate outcome measures that accurately reflect treatment benefits. Optical Coherence Tomography (OCT) provides high-resolution retinal images, serving as a valuable tool for deriving potential outcome measures, such as retinal thickness. However, automated segmentation of OCT images, particularly in regions disrupted by degeneration, remains complex. In this study, we propose a deep learning-based approach that incorporates a pathology-aware loss function to segment retinal sublayers in OCT images from patients with STGD1. This method targets relatively unaffected regions for sublayer segmentation, ensuring accurate boundary delineation in areas with minimal disruption. In severely affected regions, identified by a box detection model, the total retina is segmented as a single layer to avoid errors. Our model significantly outperforms standard models, achieving an average Dice coefficient of for total retina and for retinal sublayers. The most substantial improvement was in the segmentation of the photoreceptor inner segment, with Dice coefficient increasing by . This approach provides a balance between granularity and reliability, making it suitable for clinical application in tracking disease progression and evaluating therapeutic efficacy.

Quantifying greenspace with satellite images in Karachi, Pakistan using a new data augmentation paradigm

Greenspaces in communities are critical for mitigating effects of climate change and have important impacts on health. Today, the availability of satellite imagery data combined with deep learning methods allows for automated greenspace analysis at high resolution. We propose a novel green color augmentation for deep learning model training to better detect and delineate types of greenspace (trees, grass) with satellite imagery. Our method outperforms gold standard methods, which use vegetation indices, by 33.1% (accuracy) and 77.7% (intersection-over-union; IoU). The proposed augmentation technique also shows improvement over state-of-the-art deep learning-based methods by 13.4% (IoU) and 3.11% (accuracy) for greenspace segmentation. We apply the method to high-resolution (0.27 m /pixel) satellite images covering Karachi, Pakistan and illuminates an important need; Karachi has 4.17 m 2 of greenspace per capita, which significantly lags World Health Organization recommendations. Moreover, greenspaces in Karachi are often in areas of economic development (Pearson’s correlation coefficient shows a 0.352 correlation between greenspaces and roads, p < 0.001), and corresponds to higher land surface temperature in localized areas. Our greenspace analysis and how it relates to infrastructure and climate is relevant to urban planners, public health and government professionals, and ultimately the public, for improved allocation and development of greenspaces.

Effect of Compression on Microstructure and Fluid Transport Properties on an Electrospun Gas Diffusion Layer for PEMFC from 3D High-Resolution Imaging

Effect of Compression on Microstructure and Fluid Transport Properties on an Electrospun Gas Diffusion Layer for PEMFC from 3D High-Resolution Imaging

W-band frequency selective digital metasurface using active learning-based binary optimization

Abstract The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning. The digital metasurface is composed of a periodic array of sub-wavelength unit cells, each containing hundreds of metal or dielectric pixels that act as binary states. By utilizing a machine learning model, we apply active learning-aided binary optimization to determine the optimal binary state configurations for a given target FSS profile. Specifically, we identify optimal designs for distinct FSS on a conventional glass substrate, with transmittance peaks at 79.3 GHz and Q-factors of 32.7.

High-resolution imaging of plasma membrane proteins

High-resolution imaging of plasma membrane proteins

JWST, ALMA, and Keck Spectroscopic Constraints on the UV Luminosity Functions at z ∼ 7–14: Clumpiness and Compactness of the Brightest Galaxies in the Early Universe

Abstract We present the number densities and physical properties of the bright galaxies spectroscopically confirmed at z ∼ 7–14. Our sample is composed of 60 galaxies at z spec ∼ 7–14, including recently confirmed galaxies at z spec = 12.34–14.18 with JWST, as well as new confirmations at z spec = 6.583–7.643 with −24 < M UV < −21 mag using ALMA and Keck. Our JWST/NIRSpec observations have also revealed that very bright galaxy candidates at z ∼ 10–13 identified from ground-based telescope images before JWST are passive galaxies at z ∼ 3–4, emphasizing the necessity of strict screening and spectroscopy in the selection of the brightest galaxies at z > 10. The UV luminosity functions derived from these spectroscopic results are consistent with a double power-law function, showing tensions with theoretical models at the bright end. To understand the origin of the overabundance of bright galaxies, we investigate their morphologies using JWST/NIRCam high-resolution images obtained in various surveys, including PRIMER and COSMOS-Web. We find that ∼70% of the bright galaxies at z ∼ 7 exhibit clumpy morphologies with multiple subcomponents, suggesting merger-induced starburst activity, which is consistent with SED fitting results showing bursty star formation histories. At z ≳ 10, bright galaxies are classified into two types of galaxies: extended ones with weak high-ionization emission lines, and compact ones with strong high-ionization lines including N iv] λ1486, indicating that at least two different processes (e.g., merger-induced starburst and compact star formation/AGN) are shaping the physical properties of the brightest galaxies at z ≳ 10 and are responsible for their overabundance.

Enhanced brightness and photostability of dye-sensitized Nd-doped rare earth nanocomposite for in vivo NIR-IIb vascular and orthotopic tumor imaging

Rare-earth doped nanoparticles (RENPs) have shown promise in biomedical imaging, particularly within the NIR-IIb region, due to their deep tissue penetration and minimal light scattering. However, challenges such as low extinction coefficients, narrow excitation spectra, and prone to quenching in aqueous environments limit their effectiveness. To overcome these obstacles, we developed a novel dye-sensitized, onion-like Nd-doped RENP nanocomposite to enhance NIR-IIb imaging performance. The onion-like Nd-RENP nanocomposite markedly enhances emission intensity at 1525 nm within the NIR-IIb range by reducing quenching and improving spectral overlap. The integration of an IR783-containing micellar layer further stabilizes the NIR dye, mitigating quenching and photobleaching. In vivo imaging studies demonstrated a 75-fold increase in luminance and a 9-fold improvement in photostability compared to free NIR dyes in aqueous solutions. Time-dependent in vivo studies confirmed the nanocomposite’s capability for prolonged imaging of vascular and tumor tissues, maintaining high-resolution images for over an hour. Additionally, the nanocomposite supported successful 3D imaging reconstruction of biological tissues. The dye-sensitized onion-like Nd-RENP nanocomposite presents a significant advancement in NIR-IIb imaging, providing enhanced brightness and photostability. Its ability to maintain clear and stable imaging over extended periods suggests potential applications in dynamic vascular and tumor-targeted imaging. This innovation holds promise for future biomedical imaging technologies, particularly in areas requiring high-resolution and long-duration monitoring.Graphical

A study on the classification of threading dislocations in GaN substrate by spot size using X-ray topography high-resolution image

A study on the classification of threading dislocations in GaN substrate by spot size using X-ray topography high-resolution image

Spectrum and Polarization-Resolved Nonlinear Optical Near-Field Imaging of Plasmonic Nanoantennas.

Plasmonic nanoantennas, which support surface plasmon resonances enabling the concentration of electromagnetic energy into subwavelength volumes, have emerged as versatile tools for a wide range of applications. However, achieving high-resolution near-field imaging with polarization and temporal sensitivity remains a significant challenge. In this work, we present a novel nonlinear optical microscopy technique based on degenerate four-wave mixing to enable spectrum- and polarization-resolved near-field imaging of plasmonic nanoantennas. By using a mid-infrared pump and near-infrared probe, we capture detailed spatial distributions of plasmon-enhanced near-field intensity and polarization characteristics, revealing distinct polarization patterns and frequency-dependent enhancements. Our method enables the observation of resonance-induced spectral shifts and subwavelength spatial resolution, offering valuable insights into plasmonic field behaviors, particularly in the mid-infrared range. This approach provides a powerful tool for exploring and understanding the complexities of plasmonic nanostructures, with significant potential for advancing nano-optics applications.

Poisson phase diversity algorithm with automatic registration for sparse fluorescent images

The phase diversity technique is a powerful tool for aberration detection and high-resolution image restoration. This is achieved by employing a pair of images, one captured in focus and the other with a specific degree of defocus. It presents a practical alternative or complementary method to adaptive optics. This paper introduces a modified phase diversity algorithm based on the Poisson noise model for biological fluorescence imaging with sparse characteristics. The proposed method has demonstrated high-precision aberration estimation and high-resolution image reconstruction. Additionally, an image registration method is proposed for image registration between the focused and defocused images. The simulation analysis and verification of the algorithm were conducted using images of microbeads and HeLa cells. For experimental purposes, actual captured fluorescence microbead images and blurred zebrafish images due to aberrations were selected. Both simulation and experimental results indicate that the method is effective and accurate.

A novel technique for non- invasive diagnosis of different skin conditions herpes virus-related.

Line-field confocal optical coherence tomography (LC-OCT) is a new noninvasive imaging technique that provides high-resolution skin imaging close to histopathology. The aim of this study was to evaluate the usefulness of LC-OCT in the diagnosis of herpes virus skin infections. Patients with a clinical or anamnestic suspicion of herpes virus infection underwent LC-OCT evaluation followed by Tzanck test. The examinations were performed in a blinded manner by different dermatologists. A total of 72 patients (M: 38; F: 34; age range: 13-84) were evaluated. In 57 cases, LC-OCT allowed the recognition of the typical herpetic intraepidermal vesicles containing giant, ballooning/multinucleated cells, as confirmed by Tzanck test. The final diagnosis was herpes simplex in 16 cases, herpes zoster in 31 cases, chickenpox in 8 cases, and Kaposi varicelliform eruption in 2 cases. In the remaining 15 cases, both LC-OCT and Tzanck test excluded the diagnosis of herpetic infections. Although the diagnosis of herpes virus skin infections is generally clinical, in minimal and/or atypical cases, LC-OCT could be a rapid and valid alternative to semi-invasive/invasive and time-consuming procedures, such as Tzanck test, viral cultures, PCR, or histopathology.

Western diet since adolescence impairs brain functional hyperemia at adulthood in mice: rescue by a balanced ω-3:ω-6 polyunsaturated fatty acids ratio.

Obesity is a devastating worldwide metabolic disease, with the highest prevalence in children and adolescents. Obesity impacts neuronal function but the fate of functional hyperemia, a vital mechanism making possible cerebral blood supply to active brain areas, is unknown in organisms fed a high-caloric Western Diet (WD) since adolescence. We mapped changes in cerebral blood volume (CBV) in the somatosensory cortex in response to whisker stimulation in adolescent, adult, and middle-aged mice fed a WD since adolescence. To this aim, we used non-invasive and high-resolution functional ultrasound imaging (fUS). We efficiently mimicked the metabolic syndrome of adolescents in young mice with early weight gain, dysfunctional glucose homeostasis, and insulinemia. Functional hyperemia is compromised as early as 3 weeks of WD and remains impaired after that in adolescent mice. These findings highlight the cerebrovascular vulnerability to WD during adolescence. In WD, ω-6:ω-3 polyunsaturated fatty acids (PUFAs) ratio is unbalanced towards proinflammatory ω-6. A balanced ω-6:ω-3 PUFAs ratio in WD achieved by docosahexaenoic acid supplementation efficiently restores glucose homeostasis and functional hyperemia in adults. WD triggers a rapid impairment in cerebrovascular activity in adolescence, which is maintained at older ages, and can be rescued by a PUFA-based nutraceutical approach.

Spatial and temporal super-resolution methods for high-fidelity solar imaging

The Sun plays a significant role in space weather by emitting energy and electromagnetic radiation that influence the environment around the Earth. Missions such as SOHO, STEREO, and SDO captured solar observations at multiple wavelengths to monitor and predict solar events. However, the data transmission from these missions is often constrained, in particular, for those operating at greater distances from Earth. This limits the availability of continuous observations. We increase the spatial and temporal resolution of solar images to improve the quality and availability of solar data. By addressing telemetry constraints and providing more detailed solar image reconstructions, we seek to facilitate a more accurate analysis of solar dynamics and improve space weather prediction. We applied deep-learning techniques, specifically, a UNet-based architecture, to generate high-resolution solar images that enhance the intricate details of the solar structures. Additionally, we used a similar architecture to reconstruct solar image sequences with a reduced temporal resolution to predict missing frames and restore temporal continuity. Our deep-learning approach successfully enhances the resolution of solar images and reveals finer details of solar structures. The model also predicts missing frames in solar image sequences, which allows more continuous observation despite telemetry constraints. These advancements contribute to a better analysis of solar dynamics and set the stage for an improved space weather forecasting and future solar physics research.

Automated Fungal Identification with Deep Learning on Time-Lapse Images

The identification of species within filamentous fungi is crucial in various fields such as agriculture, environmental monitoring, and medical mycology. Traditional identification methods based on morphology have a low demand for advanced equipment usage and heavily depend on manual observation and expertise. However, this approach may struggle to differentiate between species in a genus due to their potential visual similarities, making the process time-consuming and subjective. In this study, we present an AI-based fungal species recognition model that utilizes deep learning techniques applied to time-lapse images. The training dataset, derived from fungi strains in the IBT Culture Collection, comprised 26,451 high-resolution images representing 110 species from 35 genera. The dataset was divided into a training set and validation subsets. We implemented three advanced deep learning architectures—ResNet50, DenseNet-121, and Vision Transformer (ViT)—to assess their effectiveness in accurately classifying fungal species. By utilizing images from early growth stages (days 2–3.5) for training and testing and later stages (days 4–7) for validation, our approach shortens the fungal identification process by 2–3 days, significantly reducing the associated workload and costs. Among the models, the Vision Transformer achieved the highest accuracy of 92.6%, demonstrating the effectiveness of our method. This work contributes to the automation of fungal identification, providing a reliable and efficient solution for monitoring fungal growth and diversity over time, which would be useful for culture collections or other institutions that handle a large number of new isolates in their daily work.

Baryonic Ecosystem IN Galaxies (BEINGMgII). Host galaxies of ultra-strong absorbers in the Subaru Hyper Suprime-Cam Survey

We study the galaxies hosting ultra-strong (USMgII) absorbers at small impact parameters of ∼ arcsecond (5 - 20 kpc) spanning a redshift range of allabszmin z using deep high-resolution images from the Hyper Suprime-Cam Subaru Strategic Survey and spectra from the Sloan Digital Sky Survey (SDSS). Our aim is to explore the physical origin of the USMgII absorbers and characterize the associated galaxies. We performed a galaxy spectral energy distribution (SED) fitting using optical and near-IR multiband data to identify potential absorber host galaxies. Further, we searched for the nebular emission line from absorber galaxies in the SDSS fiber spectra. From a total of USMgII absorbers with ge 3 Å, along quasar sight lines, we detected galaxies based on nebular emission detected at the ge 2σ level. Utilizing the emission from the stacked spectrum and employing the best-fit galaxy SED template, we further identified galaxies, leading to a total of bona fide USMgII galaxies. With a prerequisite of having a minimum of four HSC passbands available, we found a detection rate of ∼ at an average impact parameter of kpc. We find that galaxies hosting USMgII systems are typically star-forming main sequence galaxies, with exhibiting a starburst nature. The non-zero emission along the "clear" sight lines, with no stellar counterpart, indicates that the USMgII absorbers may likely emanate from the unseen faint galaxies near the quasar. The USMgII absorbers preferentially align along the major and minor axes of the galaxy, which suggests that they originate in the disk or large-scale wind. We show that the distribution of as a function of the impact parameter indicates a discernible radial dependence for the "disk" and "wind" subsets, with the observed large scatter in potentially attributed to large-scale outflows. The quasar sight line hosting USMgII systems show a factor of three higher galaxy surface density at impact parameters of lesssim 50kpc, highlighting the multiple pathways that give rise to USMgII absorption.

A 60-channel high-density flexible receive array for pediatric abdominal MRI.

Conventional MRI coils offer suboptimal parallel imaging performance for young children. Our goal was to enhance imaging acceleration by dedicated, flexible high-density coil design for pediatric patients at 3T. We design, construct, and evaluate a highly flexible small loop array. Key design notes include full-wave simulation and analysis of the dual-turn loop, miniature feedboard allocation at the loop center, and cable management. Phantom experiments and adult and pediatric volunteer case studies were conducted to evaluate the small loop array imaging performance compared to commercial reference coils. Dual-turn loop configuration forms higher preamp decoupling impedance than the same size single-turn, supporting a flexible form factor that requires a wide range of critical overlap. Both phantom and in-vivo studies demonstrate superior parallel imaging performance or high spatial resolution imaging using the small loop, compared to commercial reference coils. A dedicated high-density coil array with a minimum inter-component interference layout design allows a flexible form factor and higher imaging accelerations. Phantom and in-vivo volunteer case studies demonstrate promising results in improving efficiency for pediatric patients in routine clinical imaging procedures.

Self-supervised constrained super-resolution fast coded spectral imaging system

Current coded aperture spectral imaging techniques suffer from slow imaging speeds and poor image quality at low sampling rates. To address these issues, we propose a self-supervised constrained super-resolution fast coded spectral imaging system by combining a physical process of spectral imaging with image fusion super-resolution techniques. We constructed a discrete cosine transform spectrometer (DCTS) and successfully acquired low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI). In order to enhance the resolution of hyperspectral images, we designed a self-supervised spectral image super-resolution network based on the physical process of spectral imaging: SSAM-Unet. This network successfully reconstructs the HR-HSI by fusing the LR-HSI with the HR-MSI. We verified the performance and generalization ability of this method through extensive experiments. Experimental results show that our proposed method can achieve good imaging results at very low sampling rates.

A Comprehensive Analysis of Soil Erosion in Coastal Areas Based on an Unmanned Aerial Vehicle and Deep Learning Approach

Soil is an important nonrenewable resource. Soil erosion is increasingly severe, and the accurate identification of soil erosion is crucial for ecological sustainability. In recent years, advancements in artificial intelligence have significantly contributed to the development of precise modeling technologies. This study utilizes high-resolution multispectral images captured by unmanned aerial vehicles and applies five machine learning models, namely convolutional neural network (CNN), support vector classification, random forest, extreme gradient boosting, and fully connected neural network, to identify regional soil erosion. The performance of each model is evaluated using F1-score, precision, and recall measurements. The results show that all models exhibit strong recognition capabilities, with CNN outperforming the others in both training and testing phases. Specifically, CNN achieved a recall rate of 0.99 on the training set and an F1-score of 0.98. Given the black-box nature of machine learning models, the shapley additive explanations method is further used for interpreting model outputs. The analysis reveals that the normalized difference salinity index and soil erodibility factor are the primary factors influencing soil erosion in the study area.

Using artifact suppression OCT based on polarization multiplexing for tomographic imaging of LCD screens

Defect detection in the inner layers of liquid crystal display (LCD) panels is crucial for the quality control of displays. Optical coherence tomography (OCT), a nondestructive, high-resolution tomographic imaging technique, has been applied in the inspection of display panels. However, the artifacts that arise in imaging multilayer samples remain a challenge. In this study, we propose and validate a method for artifact removal in coherent imaging of multilayer refractive samples. These samples exhibit complex autocorrelation artifacts due to self-interference and multiple reflections. Two orthogonally polarized reference beams with fixed phase shifts, combined with the proposed algorithm, are employed to suppress the autocorrelation in the sample light path. To address the issue of mirror artifacts caused by real spectrum signals, the intensity ratio of the two orthogonally polarized reference beams is adjusted, allowing distinction between real signals and mirror artifacts, thus achieving full-range imaging. Experiments were conducted to measure 3D images of multilayer quartz glass sheets, inclined ceramic blocks, and LCD panels, validating the reliability of the proposed approach and demonstrating its advantages in display defect detection.

A new narrower circumscription and a nomenclatural synopsis for Calea subg. Meyeria (Asteraceae: Neurolaeneae)

The studies on Calea subg. Meyeria (Neurolaeneae, Asteraceae), a group from open vegetation in Southeastern South America, recently resulted in the description of novel species and nothospecies. Continued progress on the nomenclatural and taxonomic studies of this subgenus is presented here. A comprehensive bibliography for species described in this section was compiled and revised to allow the proposition of nomenclatural changes. All type specimens were consulted either in person or using high-resolution digital images. Based on these efforts, we propose a narrower circumscription of Calea subg. Meyeria, altering the number of accepted taxa assigned to it from 15 to 9. The subgenus is here proposed to be characterized morphologically by shrubs with a cymose capitulescence, capitula with four or more foliaceous outer phyllaries and cypsela longer than the pappus. As here defined, it comprises eight species (C. funkiana, C. marginata, C. myrtifolia, C. parvifolia, C. phyllolepis, C. pruskiana, C. subintegerrima, and C. triantha) and one nothospecies (C. × parviantha). Altogether, we synonymize four names; propose lectotypifications for two names; recognize three new isolectotypes; and provide corrections for type specimen collection data from six taxa, including collector names, collection date, and locality.