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In this study, a novel optical super-resolution imaging technique, grating and deep neural network assisted super-resolution microscopy, is proposed. The technique utilizes a sub-wavelength grating, placed between the sample and the microscope objective, to convert the evanescent waves of a sample surface into propagating waves, allowing more high spatial-frequency information of the sample to be detected in the far field. Then, the far-field image of the sample is captured and trained end-to-end with a customized deep neural network model to heuristically reconstruct a clear image of the sample with structural features smaller than λ/3. Compared with the existing super-resolution imaging techniques, the proposed technique has the advantages of label-free, large field of view, one-time direct imaging, and white light illumination and observation in an atmospheric environment. Moreover, it has the flexibility to replace raster and network rendering components according to specific inspection requirements to meet diverse application scenarios. [ABSTRACT FROM AUTHOR]

The population-averaged contact maps generated by the chromosome conformation capture technique provide important information about the average frequency of contact between pairs of chromatin loci as a function of the genetic distance between them. However, these datasets do not tell us anything about the joint statistics of simultaneous contacts between genomic loci in individual cells. This kind of statistical information can be extracted using the single-cell Hi-C method, which is capable of detecting a large fraction of simultaneous contacts within a single cell, as well as through modern methods of fluorescent labeling and super-resolution imaging. Motivated by the prospect of the imminent availability of relevant experimental data, in this work, we theoretically model the joint statistics of pairs of contacts located along a line perpendicular to the main diagonal of the single-cell contact map. The analysis is performed within the framework of an ideal polymer model with quenched disorder of random loops, which, as previous studies have shown, allows us to take into account the influence of the loop extrusion process on the conformational properties of interphase chromatin. [ABSTRACT FROM AUTHOR]

Acoustic metamaterial concepts, such as metalenses and hyperlenses, have been studied with much interest for super-resolution imaging. However, the physics of wave scattering by sub-wavelength sized defects is still not very well understood. Here, we present studies on acoustic wave interactions with circular defects of sizes varying from over a wavelength to sub-wavelength scale. Near- and far-field scattering patterns of single circular defects are presented, connecting them to analytical frameworks. Scattering field signatures produced by two identical circular defects of different sizes placed side by side are then discussed. The separation distance between the defects is among the parameters varied to understand the effects of geometric features in successful imaging. Experimental results are presented to support the predictions reported in the paper. The insights have important implications for sub-wavelength imaging using acoustic metamaterials. [ABSTRACT FROM AUTHOR]

Due to the difficulty of collecting a sufficient number of photons within a narrow spectral window, hyperspectral images often have low spatial resolution. In order to solve this problem without increasing hardware costs, a large number of image super-resolution algorithms have been proposed and have shown promising results. However, the performance of these algorithms is inconsistent in different datasets. To fix this situation, we proposed a multi-model deep prior regularization method for multispectral and hyperspectral image fusion. This method leverages mechanistic knowledge to combine various super-resolution results into a single algorithm, significantly improving the quality of the hyperspectral images. Specifically, we first proposed a variable residual mixed attention network. This network takes a low-resolution hyperspectral image and a high-resolution multispectral image as inputs, producing deep prior results from three different modes by adjusting spatial and channel attention. Then, we reconstructed the prior results along the spectral dimension and used them as regularization terms in our mechanistic model. We transformed the problem of finding the optimal solution of the model into solving the Sylvester equation and applied a fast solver for this equation to create the high-resolution hyperspectral images. Additionally, we used a genetic algorithm to determine the best hyperparameters for different deep prior results. Through validation on three datasets, we have demonstrated that our method performs better than existing methods in both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions. [ABSTRACT FROM AUTHOR]

Self-labeling protein (SLP) tags, such as HaloTag, have gained considerable interest as advanced tools for live cell labeling. However, the chloroalkane-based substrates that can be directly used for protein labeling are limited. Here, we report two bioorthogonal small molecule linkers, chloroalkane-tetrazine (CA-Tz) and chloroalkane-azide (CA-N3), which can penetrate cell membranes and facilitate click chemistry-based labeling in live cells. We compare their labeling capability using two clickable silicon rhodamine dyes (SiR-PEG3-TCO and SiR-PEG4-DBCO). Confocal imaging results demonstrate that using CA-Tz and SiR-PEG3-TCO dye exhibits superior intracellular labeling with low nonspecific signals. We subsequently compared the photostability of SiR dyes with that of green fluorescent proteins (mEmerald). Total internal reflection fluorescence (TIRF) imaging indicates that SiR dyes exhibit superior photostability under identical excitation conditions, making them suitable for long-term cell imaging. Furthermore, SiR dyes labeling also shows high structure retention for the fourth-order super-resolution optical fluctuation imaging (SOFI) compared to fluorescent proteins. This study presents clickable HaloTag linkers as effective tools for live cell labeling and imaging, highlighting the high-quality labeling of chloroalkane linkers and clickable dyes for live cell imaging. [ABSTRACT FROM AUTHOR]

Phototheranostics with second near‐infrared (NIR‐II) emissions show great potential for disease diagnosis and imaging‐guided phototherapy owing to deep tissue penetration, high imaging resolution, and excellent tumor eradication. Recently, molecular conjugation engineering and J‐aggregation have been used to construct organic NIR‐II materials. However, these molecules generally have extensive conjugation and large molecular weight in the range of 700–1700 g mol−1, requiring complicated molecular design and synthesis. Herein, a NIR‐II emissive through‐space charge‐transfer (TSCT) nanoparticle (NP) using short‐conjugated donor‐acceptor (D‐A) molecules (TTP) is reported for high‐performance bioimaging and cancer phototheranostics. Owing to the short conjugation of the TTP molecule with a small molecular weight of only 518 g mol−1, the TTP monomer possesses visible absorption and first near‐infrared (NIR‐I) emission. Upon forming NPs in water, the efficient TSCT between TTP monomers leads to significantly red‐shifted absorption to the NIR‐I and emission to the NIR‐II region with a tail that extends to 1400 nm. TTP NPs are employed in NIR‐II in vivo blood‐vessel bioimaging and cancer phototheranostics successfully. This work introduces a facile strategy to construct NIR‐II emissive NPs based on short‐conjugated D‐A molecules for high‐performance biomedical applications. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sensing and control of size are critical for cellular function and survival. A striking example of size sensing occurs during meiosis in the nematode Caenorhabditis elegans. C. elegans chromosomes compare the lengths of the two chromosome "arms" demarcated by the position of their single off-center crossover, and they differentially modify these arms to ensure that sister chromatid cohesion is lost specifically on the shorter arm in the first meiotic division, while the longer arm maintains cohesion until the second division. While many of the downstream steps leading to cohesion loss have been characterized, the length-sensing process itself remains poorly understood. Here, we have used cytological visualization of short and long chromosome arms, combined with quantitative microscopy, live imaging, and simulations, to investigate the principles underlying length-sensitive chromosome partitioning. By quantitatively analyzing short-arm designation patterns on fusion chromosomes carrying multiple crossovers, we develop a model in which a short-arm-determining factor originates at crossover designation sites, diffuses within the synaptonemal complex, and accumulates within crossover-bounded chromosome segments. We demonstrate experimental support for a critical assumption of this model: that crossovers act as boundaries to diffusion within the synaptonemal complex. Further, we develop a discrete simulation based on our results that recapitulates a wide variety of observed partitioning outcomes in both wild-type and previously reported mutants. Our results suggest that the concentration of a diffusible factor is used as a proxy for chromosome length, enabling the correct designation of short and long arms and proper segregation of chromosomes. [Display omitted] • CO-delimited chromosome functional arms are established in mid-meiotic prophase • Both phosphorylated and non-phosphorylated SYP-1 proteins partition to short arms • Multiple COs can act additively on domains to establish "short" or "long" identity • CO sites block diffusion along the SC, enabling potential concentration of factors The holocentric chromosomes of C. elegans lose cohesion at meiosis I in the shorter of the two domains defined by the single crossover. Rodriguez-Reza et al. provide evidence that the sensing of short versus long domains relies on comparing the concentration of crossover-derived factors accumulating within these domains as a proxy for physical length. [ABSTRACT FROM AUTHOR]

Mn5+ emission is a promising candidate for imaging deep tissue structures (e.g., vessels, tumors) in the second near‐infrared (NIR‐II, 1000–1350 nm) region. However, its practical application is impeded by the limited quantum efficiency of the available phosphors due to the unstable valence state of Mn5+. Herein, a novel strategy involving site competition is proposed to stabilize the Mn5+ state by the introduction of valence‐unstable Bi2+/3+. The results demonstrate that Bi3+ ions tend to occupy two different Ca2+ ion sites within the Ca6Ba(PO4)4O lattice. The incorporation of a small amount of Bi3+ effectively suppresses the amount of Mn2+ in Ca2+ sites. This is also confirmed by spectroscopic experiments and density function theory calculations. Notably, an ultra‐high internal quantum efficiency of 82.3% is achieved under excitation at 653 nm, surpassing more than twofold the previously reported value of 37.5% in Ca6Ba(PO4)4O: Mn5+. As a proof of concept, deep tissue imaging with a penetration depth of ≈2.8 cm is achieved using a self‐produced NIR‐II light‐emitting diodes device embedded with Ca6Ba(PO4)4O: 0.003Mn5+/0.003Bi3+ powder. These findings provide valuable insights into improving the luminescent properties associated with Mn5+ ions and pave the way for deep tissue imaging with high spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) algorithms based on deep learning yield good visual performances on visible images. Due to the blurred edges and low contrast of infrared (IR) images, methods transferred directly from visible images to IR images have a poor performance and ignore the demands of downstream detection tasks. Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. Furthermore, deformable convolution is utilized to fuse features extracted from different branches, enabling adaptation to targets of various shapes. To enhance the detection performance of low-resolution (LR) images, we crop the images into patches based on target labels before feeding them to the network. This allows the network to focus on learning the reconstruction of the target areas only, reducing the interference of background areas in the target areas' reconstruction. Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. This method has been tested on the FLIR Thermal Dataset and the M3FD Dataset and compared with five mainstream SR algorithms. The final results demonstrate that our method effectively maintains image texture details. More importantly, our method achieves 80.55% mAP, outperforming other methods on FLIR Dataset detection accuracy, and with 74.7% mAP outperforms other methods on M3FD Dataset detection accuracy. [ABSTRACT FROM AUTHOR]

Recently, Transformer has achieved remarkable success in image super-resolution (SR). However, most existing methods adopt the window-based self-attention to balance performance and computational overhead, which prevents Transformer from capturing global information and modeling long-range dependencies at the early layers, resulting in missing certain crucial high-frequency components. To address the above issue, we propose a novel detail-enhanced Transformer for image super-resolution (DTSR) that exploits gradient information to learn high-frequency components and perceives more abundant global contextual features. Specifically, the proposed DTSR consists of a super-resolution branch and a gradient branch, with the former used to capture texture information and the latter used to gather edge and structure information to compensate for the missing high-frequency components in the former. Furthermore, to aggregate the generated features from both branches, we propose a channel-wise-spatial locally-enhanced self-attention layer to model local and global dependencies, and then further incorporate it to construct a cross locally-enhanced self-attention block to extract local features within a patch as well as global contextual features between patches in a single-channel feature map. Finally, the bidirectional fusion strategy enables the fused features to complement each branch to further promote the feature representation. Extensive experiments validate that our DTSR significantly outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. [ABSTRACT FROM AUTHOR]

In synchrotron X‐ray radiography, achieving high image resolution and an optimal signal‐to‐noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X‐rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X‐ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post‐processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X‐ray imaging beamline setups. This facilitates further image analysis and processing using established methods. [ABSTRACT FROM AUTHOR]

The Sentinel-2 image is widely used for cropland mapping, but the limitation of its 10 m spatial resolution may significantly impact the accuracy of result in areas characterized by severe cropland fragmentation. To address the issue, this letter proposes a super-resolution cropland mapping model for Sentinel-2 images. The proposed model improves the spatial resolution of Sentinel-2 images to 2.5 m through self-training learning with the Swin Transformer for Image Restoration (SwinIR) model without needing fine-resolution training samples. Then, the random forest classification is applied to map cropland from the super-solved 2.5 m resolution image. The proposed model was assessed in the Jianghan Plain, China, and the results show that the super-resolution images produce cropland maps with higher accuracy than the original Sentinel-2 images. Comparing cropland mapping results before and after super-resolution, the overall accuracy improves from 0.82 to 0.89, while the commission error and omission error decrease from 0.09 to 0.05 and 0.17 to 0.11, respectively. This method addresses the shortage of training samples collection and improves the accuracy of cropland mapping, contributing to more reliable agricultural remote sensing applications. [ABSTRACT FROM AUTHOR]

This is a summary of a consensus statement on the introduction of "Ultrasound microvasculomics" produced by The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The evaluation of microvessels is a very important part for the assessment of diseases. Super-resolution ultrasound (SRUS) microvascular imaging surpasses traditional ultrasound imaging in the morphological and functional analysis of microcirculation. SRUS microvascular imaging relies on contrast microbubbles to gain sensitivity to microvessels and improves the spatial resolution of ultrasound blood flow imaging for a more detailed depiction of vascular structures and hemodynamics. This method has been applied in preclinical animal models and pilot clinical studies, involving areas including neurology, oncology, nephrology, and cardiology. However, the current quantitative parameters of SRUS images are not enough for precise evaluation of microvessels. Therefore, by employing omics methods, more quantification indicators can be obtained, enabling a more precise and personalized assessment of microvascular status. Ultrasound microvasculomics - a high-throughput extraction of image features from SRUS images - is one novel approach that holds great promise but needs further validation in both bench and clinical settings. Clinical relevance statement: Super-resolution Ultrasound (SRUS) blood flow imaging improves spatial resolution. Ultrasound microvasculomics is possible to acquire high-throughput information of features from SRUS images. It provides more precise and abundant micro-blood flow information in clinical medicine. Key Points: This consensus statement reviews the development and application of super-resolution ultrasound (SRUS). The shortcomings of the current quantification indicators of SRUS and strengths of the omics methodology are addressed. "Ultrasound microvasculomics" is introduced for a high-throughput extraction of image features from SRUS images. [ABSTRACT FROM AUTHOR]

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264 nm to 138 nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels. [ABSTRACT FROM AUTHOR]

The development of telescopes with large apertures is driven by the increasing demand for higher imaging resolution and coverage. Large membrane diffraction space telescopes are particularly attractive due to their lightweight nature, ease of folding and unfolding, and high-precision imaging capabilities. However, their substantial size and flexibility make them susceptible to long-duration, low-frequency vibrations during attitude maneuvers, which degrade attitude and imaging accuracy and risk instrument damage. Consequently, an urgent need exists for a high-precision integrated control scheme. In this paper, the integrated control of vibration and attitude in a large space telescope is studied using cable actuators and control moment gyroscopes (CMGs). Since cables can only withstand limited tension, the unilateral and constrained characteristics of the control input are taken into account during the control design process. Firstly, a rigid-flexible coupling dynamic model of the space telescope is established using the velocity variation principle with hybrid coordinates. Additionally, a two-body astrodynamic model is developed to assess the impact of gravity gradient torque. Next, combining the computational torque method and H∞ control theory, an integrated control scheme is proposed, which achieves vibration and attitude control during maneuvers. Then, this paper optimizes the cable actuator positions via the discrete particle swarm optimization (PSO) algorithm and plans various attitude maneuver paths. Finally, numerical simulations are adopted to demonstrate the effectiveness of the control scheme. The results show that this integrated control scheme swiftly suppresses structural vibrations during attitude maneuvers, enabling high-precision attitude control of the space telescope. • A vibration-attitude integrated control for a novel large membrane diffraction space telescope is studied • 3 types of attitude maneuver paths are planned for the two requirements of time optimal and minimum vibration excitation. [ABSTRACT FROM AUTHOR]

Training deep neural networks for semantic segmentation of aerial images relies heavily on obtaining a large number of precise pixel-level annotations, which can cause significant annotation expenses. Given the fact that acquiring fine-class annotations is considerably more challenging than obtaining coarse-class annotations, we present a novel semi-supervised learning framework, which utilizes high spatial resolution images annotated with coarse-class labels alongside a very small set of fine-grained annotated images as the training set, thereby achieving classification results that are refined in both spatial resolution and categorical granularity. Specifically, this framework adopts Mix Transformer (MiT) as the backbone architecture to accommodate both local feature extraction and long-range dependency modeling capabilities and utilizes multi-prototype learning to model each class as multiple sub-prototypes, preserving the intrinsic variance characteristics within classes. We propose a dedicated co-training approach tailored for extracting fine-grained pseudo-labels from coarse-grained samples. In this approach, a local-softmax pseudo-labeling strategy is developed to ensure a harmonious balance between the efficiency and accuracy of the pseudo-labeling, and four losses are formulated for both single-level class and cross-category granularity supervised learning. We evaluate the proposed framework on the Gaofen Image Dataset (GID) and Five-Billion-Pixels (FBP) dataset, confirming its feasibility and superior results. In particular, based on coarse-class annotations, the performance achieved using only 5% of fine-class labels, in terms of the four metrics, namely mIoU, mean UA, mean F1-score, and OA, reached 91%, 96%, 89%, and 93% of the fully-supervised baseline performance respectively. The code is available at https://github.com/chenhaocs/C2F. [ABSTRACT FROM AUTHOR]

The spatial and spectral features of hyperspectral images exhibit complementarity, and neglecting them prevents the full exploitation of useful information for superresolution. This article proposes a spatialspectral middle cross-attention fusion network to explore the spatialspectral structure correlation. Initially, we learn spatial and spectral features through spatial and spectral branches instead of single ones to reduce information compression. Then, a novel middle-cross attention fusion block that includes middle features fusion strategy and crossattention is proposed to fuse spatial-spectral features to enhance their mutual effects, which aims to explore the spatial-spectral structural correlations. Finally, we propose a spectral feature compensation mechanism to provide complementary information for adjacent band groups. The experimental results show that the proposed method outperforms state-of-the-art algorithms in object values and visual quality. [ABSTRACT FROM AUTHOR]

Structured beams carrying orbital angular momentum (OAM) provide powerful capabilities for applications in optical tweezers, super-resolution imaging, quantum optics, and ad-vanced microparticle manipulation. However, it is challenging for generate and control the OAM beams at the extreme ultraviolet (EUV) region due to the lack of suitable wave front shaping optics arise from being limited to the strong absorption of most materials. Here, we use a modified Fermat-spiral photon-sieve splitter to simultaneously generate two focused doughnut beams with opposite helical phase. Our technique enables us to produce splitting focused vortex beams with different rotation directions at EUV wavelengths. Additionally, we provide experimental evidence showcasing the capabilities of our method and further detect the helical phase by self-reference interferometry. This work not only opens a route for OAM-driven applications in EUV radiation, but also paves the way to studies of holographic technique by EUV splitter. [ABSTRACT FROM AUTHOR]

Understanding the architecture of the human deep brain is especially challenging because of the complex organization of the nuclei and fascicles that support most sensorimotor and behaviour controls. There are scant dedicated tools to explore and analyse this region. Here we took a transdisciplinary approach to build a new deep-brain MRI architecture atlas drawing on advanced clinical experience of MRI-based deep brain mapping. This new tool comprises a young-male-adult MRI template spatially normalized to the ICBM152, containing T1, inversion-recovery, and diffusion MRI datasets (in vivo acquisition), and an MRI atlas of 118 labelled deep brain structures. It is open-source and gives users high resolution image datasets to describe nuclear-based and axonal architecture, combining pioneering and recent knowledge. It is a useful addition to current 3D atlases and clinical tools. [ABSTRACT FROM AUTHOR]

Purpose: T 2$$ {}_2 $$‐weighted turbo‐spin‐echo (TSE) sequences are a fundamental technique in brain imaging but suffer from field inhomogeneities at ultra‐high fields. Several methods have been proposed to mitigate the problem, but were limited so far to nonselective three‐dimensional (3D) measurements, making short acquisitions difficult to achieve when targeting very high resolution images, or needed additional calibration procedures, thus complicating their application. Methods: Slab‐selective excitation pulses were designed for flexible placement utilizing the concept of k T$$ {}_T $$‐spokes. Phase‐coherent refocusing universal pulses were subsequently optimized with the Gradient Ascent Pulse Engineering algorithm and tested in vivo for improved signal homogeneity. Results: Implemented within a 3D variable flip angle TSE sequence, these pulses led to a signal‐to‐noise ratio (SNR) improvement ranging from 10% to 30% compared to a two‐dimensional (2D) T2w TSE sequence employing B1+$$ {\mathrm{B}}_1^{+} $$‐shimmed pulses. B1+$$ {\mathrm{B}}_1^{+} $$ field inhomogeneities could be mitigated and artifacts from B0$$ {\mathrm{B}}_0 $$ deviations reduced. The concept of universal pulses was successfully applied. Conclusion: We present a pulse design method which provides a set of calibration‐free universal pulses (UPs) for slab‐selective excitation and phase‐coherent refocusing in slab‐selective TSE sequences. [ABSTRACT FROM AUTHOR]

Purpose: Non‐contrast‐enhanced time of flight (TOF) is a standard method for magnetic resonance angiography used to depict vessel morphology. TOF is commonly performed with a 3D steady‐state acquisition, employing a short repetition time to support high resolution imaging. At 7 T, TOF exhibits substantial increase in SNR and contrast, improving its clinical value. However, one of the remaining challenges, exacerbated at 7 T, is the presence of artifacts due to pulsatile blood flow, especially near major blood vessels. In this study we examine a method to significantly reduce these artifacts. Methods: We recently introduced a new "local‐scrambling" approach that semi‐randomizes the acquisition order of the phase encodes, to achieve a controllable cutoff frequency above which the artifacts are drastically reduced. With this approach, artifacts resulting from fast local fluctuations such as cardiac pulsation are significantly reduced. In this study, we explore the ability of this local‐scrambling approach to reduce pulsatile blood flow artifacts in a 3D TOF acquisition. Cartesian line‐by‐line and center‐out ordering, with and without local‐scrambling, were compared in simulations and in human brain imaging at 3 and 7 T scanners. Results: In the simulations the artifact intensity showed a 10‐fold reduction using local‐scrambling compared to line‐by‐line and 4‐fold compared to center‐out ordering. In vivo results show that artifacts are much more pronounced at 7 T compared to 3 T, and in both cases they are effectively reduced by local‐scrambling. Conclusion: Local‐scrambling improves image quality for both line‐by‐line and center‐out ordering. This approach can easily be implemented in the scanner without any changes to the reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: There is a need for high resolution non‐invasive imaging methods of physiologic magnetic fields. The purpose of this work is to develop a MRI detection approach for non‐sinusoidal magnetic fields based on the rotary excitation (REX) mechanism which was previously successfully applied for the detection of oscillating magnetic fields in the sub‐nT range. Methods: The new detection concept was examined by means of Bloch simulations, evaluating the interaction effect of spin‐locked magnetization and low‐frequency pulsed magnetic fields. The REX detection approach was validated under controlled conditions in phantom experiments at 3 T. Gaussian and sinc‐shaped stimuli were investigated. In addition, the detection of artificial fields resembling a cardiac QRS complex, which is the most prominent peak visible on a magnetocardiogram, was tested. Results: Bloch simulations demonstrated that the REX method has a high sensitivity to pulsed fields in the resonance case, which is met when the spin‐lock frequency coincides with a non‐zero Fourier component of the stimulus field. In the experiments, we found that magnetic stimuli of different durations and waveforms can be distinguished by their characteristic REX response spectrum. The detected REX amplitude was proportional to the stimulus peak amplitude (R2 > 0.98) and the lowest field detection was 1 nT. Furthermore, the detection of QRS‐like fields with varying QRS durations yielded significant results in a phantom setup (p < 0.001). Conclusion: REX detection can be transferred to non‐sinusoidal pulsed magnetic fields and could provide a non‐invasive, quantitative tool for spatially resolved assessment of cardiac biomagnetism. Potential applications include the direct detection and characterization of cardiac conduction. [ABSTRACT FROM AUTHOR]

Introduction/Aims Methods Results Discussion Ultrahigh‐frequency ultrasound (UHFUS) allows improved visualization and higher resolution images of nerve fascicles than standard high‐frequency ultrasound. Dynamic UHFUS may detect the presence of fascicular entwinement, the recently described sonographic phenomenon of pathologic fascicular rotation seen in neuralgic amyotrophy. This pilot study aims to establish normative reference values and degrees of fascicular rotation for the proximal portions of commonly involved upper limb nerves in healthy controls using UHFUS.Twenty healthy participants underwent sonographic examination of the median, musculocutaneous, and radial nerves on both upper limbs using UHFUS with a 48 MHz linear transducer. A single rater assessed the degree of fascicular rotation in each peripheral nerve.Fascicular rotation appears to occur in the proximal portion of each of these nerves. The mean degree of fascicular rotation for each of the measured nerves was median 94.5°, musculocutaneous 97.9°, and radial 50.9°. The maximum observed fascicular rotation in each nerve was 180°. Age, sex, height, weight, body mass index, and race did not predict degree of fascicular rotation (all p > .103). A single‐factor ANOVA test showed the degree of fascicular rotation differed in median, musculocutaneous, and radial nerves (F = 4.748, p = .011).UHFUS allows quantification of fascicular rotation in healthy controls in the median, musculocutaneous, and radial nerves, and provides normative data. The data from this pilot study may serve as control data for future comparative studies in conditions where fascicular rotation occurs, such as neuralgic amyotrophy. [ABSTRACT FROM AUTHOR]

Super-resolution structured illumination microscopy (SR-SIM) relies heavily on post-processing reconstruction to obtain high-quality SR images from raw data. Although many SIM reconstruction algorithms have been developed to recover fine cellular structures with high fidelity even from the noisy data, whether the pixel intensities of reconstructed SR images are still proportional to the original fluorescence intensity has been less explored. The linearity between the intensity before and after reconstruction is defined as the intensity fidelity. Here, we proposed a method to evaluate the reconstructed SR image intensity fidelity at different spatial frequencies. With the proposed metric, we systematically investigated the impact of the key factors on the intensity fidelity in the standard Wiener-SIM reconstructions with simulated data, then evaluated the intensity fidelity of the SR images reconstructed by representative open-source packages. Our work provides a reference for SR-SIM image intensity fidelity improvement. [ABSTRACT FROM AUTHOR]

The exploration of multi-dimensional brain activity with high temporal and spatial resolution is of great significance in the diagnosis of neurological disease and the study of brain science. Although the integration of electroencephalogram (EEG) with magnetic resonance imaging (MRI) and computed tomography (CT) provides a potential solution to achieve a brain-functional image with high spatiotemporal resolution, the critical issues of interface stability and magnetic compatibility remain challenging. Therefore, in this research, we proposed a conductive hydrogel EEG electrode with an asymmetrical bilayer structure, which shows the potential to overcome the challenges. Benefiting from the bilayer structure with different moduli, the hydrogel electrode exhibits high biological and mechanical compatibility with the heterogeneous brain-electrode interface. As a result, the impedance can be reduced compared with conventional metal electrodes. In addition, the hydrogel-based ionic conductive electrodes, which are free from metal conductors, are compatible with MRI and CT. Therefore, they can obtain high spatiotemporal resolution multi-dimensional brain information in clinical settings. The research outcome provides a new approach for establishing a platform for early diagnosis of brain diseases and the study of brain science. [ABSTRACT FROM AUTHOR]

In recent years, numerous CNN-based light field (LF) image super-resolution (SR) methods have been developed. However, due to the downsampling inconsistency between low-resolution (LR) testing LF images and LR training LF images, they may suffer from quality degradation. To address this quality degradation issue, this paper proposes a downsampling consistency correction-based (DCC-based) quality enhancement method. Firstly, a quality-based voting strategy is introduced to identify the downsampling scheme used in the training step. Next, a cascaded Swin Transformer-based recognizer is proposed to identify the downsampled position and downsampling scheme used in the LR testing LF image, after which the proposed DCC-based method is employed to significantly improve the quality of the upsampled LF image. Comprehensive experiments have been conducted on typical LF image datasets to demonstrate the significant quality improvement achieved by our method in comparison to state-of-the-art LF SR methods. [ABSTRACT FROM AUTHOR]

Face super-resolution, commonly referred to as face hallucination, is a specific domain of super-resolution that focuses on generating high-resolution face images from their corresponding low-resolution versions. Face hallucination has received evident advances and significant attention with the expansion of deep learning methods. However, the state-of-the-art methods do not show much ability to preserve the inherent structure of the image. To address this limitation, we propose an Edge-Attention architecture that effectively maintains both local and global information in face super-resolution. Additionally, we introduce a novel loss function, the Frechet VGG Distance, which minimizes the discrepancy between the features of generated images and ground-truth images, thereby producing more realistic and high-quality results. Furthermore, we leverage the use of Unsharp Masking (UM) and Local Binary Pattern (LBP) techniques to enhance the edges of the low-resolution image. By employing the LBP method to refine the delicate edges and applying Unsharp Masking to sharpen all edges, our network excels at retrieving image structures in the super-resolution process. Experimental results provide evidence that our approach outperforms existing face super-resolution approaches in preserving facial structure and generating exceptional image quality. Moreover, our proposed method exhibits remarkable performance in enhancing the accuracy of face recognition systems when working with real-world low-resolution facial images. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics.As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 ×$\times$ 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts.Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts.This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) have great improvements for single image super resolution (SISR). However, most of the existing SISR pre-training models can only reconstruct low-resolution (LR) images in a single image, and their upsamling factors cannot be non-integers, which limits their application in practical scenarios. In this letter, we propose a multi-scale cross-fusion network (MCNet) to accomplish the super-resolution task of images at arbitrary scale. On the one hand, the designed scale-wise module (SWM) combine the scale information and pixel features to fullly improve the representation ability of arbitrary-scale images. On the other hand, we construct a multi-scale cross-fusion module (MSCF) to enrich spatial information and remove redundant noise, which uses deep feature maps of different sizes for interactive learning. A large number of experiments on four benchmark datasets show that the proposed method can obtain better super-resolution results than existing arbitrary scale methods in both quantitative evaluation and visual comparison. [ABSTRACT FROM AUTHOR]

Light field (LF) image super-resolution (SR) is a technique designed to enhance the details and clarity of low-resolution (LR) light field images by leveraging the additional information and structure present within the LF data. With the rise of deep learning, the performance of LF image super-resolution has been significantly improved, but it has led to an increase in model parameters and computational complexity, resulting in a phenomenon of excessive reliance on computational resources. To address this problem, this paper proposes a lightweight but effective model. In our approach, we employ Swin Attention to extract features from LF images. Window and Shifted Window are the main components of Swin Attention. Therefore, the local features of the LF images are captured using Window, and feature correlations are established through Shifted Window. Furthermore, we introduce Extensive Attention (EA) blocks to capture the global features of the LF image. In addition to the aforementioned configurations, we have also engineered an iteration of the Low Computation Convolution (LCC) that is capable of eliminating redundant information prior to the feature extraction process in LF images. This design aims to mitigate superfluous computations, thereby enhancing computational efficiency. Experimental results show that our approach achieves suboptimal performance compared to other state-of-the-art models, while having fewer parameters, lower computational complexity, and faster inference speed. [ABSTRACT FROM AUTHOR]

With the development of edge intelligent devices such as unmanned aerial vehicles (UAVs), the demand of for high-resolution (HR) images increase significantly. However, noise and blurring from finite inspector sizes and optics make high-resolution images difficult to acquire directly. Therefore, lightweight super-resolution of optical images based on convolutional neural network (CNN) has become a hot spot. While most state-of-the-art methods pay more attention to local features on a particular dimension. Though composite attention mechanism has employed in them, the conflict among features of different attention types affects the SR performance thoroughly. In this paper, we propose a dynamic feature distillation and pyramid split large kernel attention network (DPLKA) to solve such problems. In particular, a pyramid split large kernel attention module (PSLKA) is introduced to obtain the multi-scale global information and long-range dependence. Subsequently, by constructing a global-to-local feature extraction block (GL-FEB), a global-to-local feature extraction approach similar to swin transformer with multi-scale self-attention is established. Furthermore, a dynamic feature distillation block (DFDB) is considered in this model with the purpose of utilizing hierarchical features from different layers and realizing adaptive recalibration of different response. Specifically, DPLKA applies lightweight architecture such as depth-wise separable convolution (SDC) and distillation feature extraction module (DFEM) which greatly improves the effectiveness of the method. Extensive experimental results on five benchmark datasets indicate that DPLKA is dominant in reconstruction accuracy (0.21 ~ 2 dB in Urban100 dataset with the scale of × 4 ), excellent running time (0.047 s in Urban100 dataset) and recipient parameters and flops. [ABSTRACT FROM AUTHOR]

The mesoscope has emerged as a powerful imaging tool in biomedical research, yet its high cost and low resolution have limited its broader application. Here, we introduce the Omni-Mesoscope, a high-spatial-temporal and multimodal mesoscopic imaging platform built from cost-efficient off-the-shelf components. This system uniquely merges the capabilities of label-free quantitative phase microscopy to capture live-cell morphodynamics across thousands of cells with highly multiplexed fluorescence imaging for comprehensive molecular characterization. This Omni-Mesoscope offers a mesoscale field of view of ~5 square millimeters with a high spatial resolution down to 700 nanometers, enabling the capture of detailed subcellular features. We demonstrate its capability in delineating molecular characteristics underlying rare morphodynamic cellular phenomena, including cancer cell responses to chemotherapy and the emergence of polyploidy in drug-resistant cells. We also integrate expansion technique to enhance three-dimensional volumetric super-resolution imaging of thicker tissues, opening the avenues for biological exploration at unprecedented scales and resolutions. [ABSTRACT FROM AUTHOR]

In eukaryotic cell nuclei, specific sets of proteins gather in nuclear bodies and facilitate distinct genomic processes. The nucleolus, a nuclear body, functions as a factory for ribosome biogenesis by accumulating constitutive proteins, such as RNA polymerase I and nucleophosmin 1 (NPM1). Although in vitro assays have suggested the importance of liquid--liquid phase separation (LLPS) of constitutive proteins in nucleolar formation, how the nucleolus is structurally maintained with the intranuclear architecture remains unknown. This study revealed that the nucleolus is encapsulated by a single-stranded (ss)DNA-based molecular complex inside the cell nucleus. Super-resolution lattice-structured illumination microscopy (lattice-SIM) showed that there was a high abundance of ssDNA beyond the 'outer shell' of the nucleolus. Nucleolar disruption and the release of NPM1 were caused by in situ digestion of ssDNA, suggesting that ssDNA has a structural role in nucleolar encapsulation. Furthermore, we identified that ssDNA forms a molecular complex with histone H1 for nucleolar encapsulation. Thus, this study illustrates how an ssDNA-based molecular complex upholds the structural integrity of nuclear bodies to coordinate genomic processes such as gene transcription and replication. [ABSTRACT FROM AUTHOR]

Eukaryotic cells are compartmentalized into membrane-bound organelles that must coordinate their responses to stimuli. One way that organelles communicate is via membrane contact sites (MCSs), sites of close apposition between organelles used for the exchange of ions, lipids and information. In this Cell Science at a Glance article and the accompanying poster, we describe an explosion of new methods that have led to exciting progress in this area and discuss key examples of how these methods have advanced our understanding of MCSs. We discuss how diffraction-limited and super-resolution fluorescence imaging approaches have provided important insight into the biology of interorganelle communication.We also describe how the development of multiple proximity-based methods has enabled the detection ofMCSswith high accuracy and precision. Finally, we assess howrecent advances in electronmicroscopy (EM), considered the gold standard for detecting MCSs, have allowed the visualization of MCSs and associated proteins in 3D at ever greater resolution. [ABSTRACT FROM AUTHOR]

As an alternative to true isotropic 3D imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3D volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3D convolutional neural networks (3D CNNs), which can effectively capture the 3D structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3D volume with a size of 256 × 256 × 256 in less than 1 min with the support of an NVIDIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one. [ABSTRACT FROM AUTHOR]

The digital number of a given pixel in a satellite image does not only measure the radiance originating from the area of the Earth’s surface represented by this pixel, but also measures radiance originating from surrounding areas represented by adjacent pixels. This adjacency effect, also known as image blurring, is a fundamental limitation of satellite images, and introduces errors in all image processing techniques that quantify the properties of the Earth’s surface on a per-pixel basis. Image deblurring via deconvolution, is a traditional technique to reduce the adjacency effect, which has proven effective in a variety of remote sensing applications. For example, it has provided an almost fourfold reduction of the adjacency error, when using support vector machine classifiers to estimate land cover proportion at a subpixel level. The remote sensing literature usually assumes that satellite images should be deconvolved with a kernel defined by the sensor’s Point Spread Function PSF(x, y), overlooking the results of three seminal empirical studies which systematically demonstrated that the optimum deconvolution kernel is a shrunk PSF of the form PSF(x/β, y/β), where β < 1 is the optimum shrink factor. The current empirical procedure to find the optimum shrink factor is laborious and restrictive since it requires suitable satellite images of much higher resolution, such that their Ground Sampling Distance (GSD) is equal to the GSD of the sensor of interest divided by an integer number much greater than one. A new theoretical procedure that uses synthetic edge images to find the optimum shrink factor is proposed and applied to the same cases considered by the current empirical procedure, showing that the same results are obtained. The new procedure is much simpler to apply and more accurate and versatile, opening a variety of research paths on satellite image deblurring. [ABSTRACT FROM AUTHOR]

High-resolution optical fluorescence microscopies and, in particular, super-resolution fluorescence microscopy, are rapidly adopting highly sensitive cameras as their preferred photodetectors. Camera-based parallel detection facilitates high-speed live cell imaging with the highest spatial resolution. Here, we show that the drive to use ever more sensitive, photon-counting image sensors in cameras can, however, have detrimental effects on the spatial resolution of the resulting images. This is particularly noticeable in applications that demand a high space-bandwidth product, where the image magnification is close to the Nyquist sampling limit of the sensor. Most scientists will often select image sensors based on parameters such as pixel size, quantum efficiency, signal-to-noise performance, dynamic range, and frame rate of the sensor. A parameter that is, however, typically overlooked is the sensor's modulation transfer function (MTF). We have determined the wavelength-specific MTF of front- and back-illuminated image sensors and evaluated how it affects the spatial resolution that can be achieved in high-resolution fluorescence microscopy modalities. We find significant differences in image sensor performance that cause the resulting spatial resolution to vary by up to 28%. This result shows that the choice of image sensor has a significant impact on the imaging performance of all camera-based optical microscopy modalities. Pixel crosstalk can significantly reduce the image resolution in standard and super-resolution light microscopy using sCMOS cameras up to 28%. The effect for different imaging modalities was evaluated for several wavelengths and image sensor types. [ABSTRACT FROM AUTHOR]

High-resolution optical images will provide planetary geology researchers with finer and more microscopic image data information. In order to maximize scientific output, it is necessary to further increase the resolution of acquired images, so image super-resolution (SR) reconstruction techniques have become the best choice. Aiming at the problems of large parameter quantity and high computational complexity in current deep learning-based image SR reconstruction methods, we propose a novel Recursive Swin Transformer Super-Resolution Network (RSTSRN) for SR applied to images. The RSTSRN improves upon the LapSRN, which we use as our backbone architecture. A Residual Swin Transformer Block (RSTB) is used for more efficient residual learning, which consists of stacked Swin Transformer Blocks (STBs) with a residual connection. Moreover, the idea of parameter sharing was introduced to reduce the number of parameters, and a multi-scale training strategy was designed to accelerate convergence speed. Experimental results show that the proposed RSTSRN achieves superior performance on 2×, 4× and 8×SR tasks to state-of-the-art methods with similar parameters. Especially on high-magnification SR tasks, the RSTSRN has great performance superiority. Compared to the LapSRN network, for 2×, 4× and 8× Mars image SR tasks, the RSTSRN network has increased PSNR values by 0.35 dB, 0.88 dB and 1.22 dB, and SSIM values by 0.0048, 0.0114 and 0.0311, respectively. [ABSTRACT FROM AUTHOR]

Fluorescence microscopy has long been a transformative technique in biological sciences. Nevertheless, most implementations are limited to a few targets, which have been revealed using primary antibodies and fluorescently conjugated secondary antibodies. Super-resolution techniques such as Exchange-PAINT and, more recently, SUM-PAINT have increased multiplexing capabilities, but they require specialized equipment, software, and knowledge. To enable multiplexing for any imaging technique in any laboratory, we developed NanoPlex, a streamlined method based on conventional antibodies revealed by engineered secondary nanobodies that allow the selective removal of fluorescence signals. We develop three complementary signal removal strategies: OptoPlex (light-induced), EnzyPlex (enzymatic), and ChemiPlex (chemical). We showcase NanoPlex reaching 21 targets for 3D confocal analyses and 5–8 targets for dSTORM and STED super-resolution imaging. NanoPlex has the potential to revolutionize multi-target fluorescent imaging methods, potentially redefining the multiplexing capabilities of antibody-based assays. Confocal and super-resolution tools used for multiplexed fluorescence imaging often demand specialized equipment and software. Here, the authors present NanoPlex, a universal method for high-plex labeling while preserving cellular ultrastructure. The approach relies on engineered secondary nanobodies that allow the selective removal of fluorescence signals. [ABSTRACT FROM AUTHOR]

Although Convolutional Neural Networks have significantly improved the development of SAR image super-resolution (SR) technology in recent years, it is a very challenging problem to reconstruct SAR image with large-scale factors, such as ×4 and ×8 due to limited available information from low-resolution image. The co-registered high-resolution optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics. Compared with single-frame SAR image SR reconstruction technology, optical image-guided SAR image SR reconstruction technology has better performance. This paper proposes an optical-guided residual dense network (OGRN) for SAR image super-resolution reconstruction network. The network is an end-to-end network. Its most important characteristic is that the SR model of SAR images can be obtained through optical image assistance during training, and high-quality SAR images can be generated without optical image assistance during testing. Firstly, a new dense residual backbone network that extracts high-frequency information is constructed. It can extract and propagate high-frequency features of SAR images efficiently to improve the feature representation ability of the network for high-frequency features. Then, the extracted high-frequency features of the SAR image are used to generate an optical image through the designed SAR-to-Optical image translation network. Finally, a reconstruction module is constructed based on the proposed fusion module for optical and SAR images. The fusion module fuses the high-frequency feature information of the extracted optical images with the SAR image features to provide features for SAR image SR reconstruction. Extensive experiments conducted on Sen1-2 and QXS datasets demonstrated that under the guidance of optical image, our OGRN can achieve excellent performance in both quantitative assessment metrics and visual quality. [ABSTRACT FROM AUTHOR]

Fourier ptychography (FP) is a high resolution wide‐field imaging method based on the extended aperture in the Fourier space, which is synthesized from raw images with varying illumination angles. If FP is extended to coherent nonlinear optical imaging, the resolution could be further improved due to the increase of the cutoff frequency of the synthesized coherent optical transfer function (C‐OTF) with respect to the order of nonlinear optical processes. However, there is a fundamental conflict between wide‐field FP and nonlinear optical imaging, whereby the nonlinear optical imaging typically requires a focused excitation laser beam with high power density. To tackle the problem, in this work, a unique point‐scanning FP (PS‐FP) method is presented for super‐resolution nonlinear optical imaging, in which the nonlinear optical signal is obtained by using focused laser beam, while the conventional FP algorithm can still be used to retrieve the super‐resolution image. PS‐FP coherent anti‐Stokes Raman scattering (PS‐FP‐CARS) imaging on a variety of samples, where a 1.8‐fold expansion of the OTF is achieved experimentally for enhancing vibrational imaging. Further theoretical calculation shows that the C‐OTF of PS‐FP higher‐order CARS (PS‐FP‐HO‐CARS) can be expanded up to ≈4.9‐fold, thereby improving the spatial resolution by ≈3‐fold in comparison with conventional point‐scanning CARS with under tightly focused beams. The generality of PS‐FP method developed in this work can be adapted to other coherent nonlinear optical imaging modalities for super‐resolution imaging in tissue and cells. [ABSTRACT FROM AUTHOR]

Morphogenesis requires building stable macromolecular structures from highly dynamic proteins. Muscles are anchored by long-lasting integrin adhesions to resist contractile force. However, the mechanisms governing integrin diffusion, immobilization, and activation within developing tissues remain elusive. Here, we show that actin polymerization-driven membrane protrusions form nanotopographies that enable strong adhesion at Drosophila muscle attachment sites (MASs). Super-resolution microscopy reveals that integrins assemble adhesive belts around Arp2/3-dependent actin protrusions, forming invadosome-like structures with membrane nanotopographies. Single protein tracking shows that, during MAS development, integrins become immobile and confined within diffusion traps formed by the membrane nanotopographies. Actin filaments also display restricted motion and confinement, indicating strong mechanical connection with integrins. Using isolated muscle cells, we show that substrate nanotopography, rather than rigidity, drives adhesion maturation by regulating actin protrusion, integrin diffusion and immobilization. These results thus demonstrate that actin-polymerization-driven membrane protrusions are essential for the formation of strong integrin adhesions sites in the developing embryo, and highlight the important contribution of geometry to morphogenesis. The mechanisms controlling the formation of cellular adhesions are not fully understood. Here, the authors use single molecule tracking and super-resolution imaging to show that the actin cytoskeleton regulates integrin diffusion in the developing Drosophila embryo. [ABSTRACT FROM AUTHOR]

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion modelddenoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research. [ABSTRACT FROM AUTHOR]

In order to obtain super-resolution images with richer details and clearer textures, a method for image super-resolution reconstruction using dense residual attention and multi-scale fusion is proposed. First, different scale convolutions are used to fully extract shallow features of the image; then high-frequency features of the image are extracted through one three-layer cascaded multi-scale feature fusion and dense residual attention module, and the reuse of feature map is achieved; finally, residual branches are used to introduce shallow features and high-frequency features of each channel image, and the high-resolution images are reconstructed through up-sampling and sub-pixel convolution. The test results on the Set5, Set14, Bsd100, and Urban100 datasets show that the PSNR and SSIM of our model are superior to most current algorithms, especially in the case of × 4 reconstruction results. PSNR has improved by 0.2 dB on the Set5 and Bsd100 datasets, and the algorithm has a better subjective visual effect. [ABSTRACT FROM AUTHOR]

Computer tomography (CT) scans' capabilities in detecting lesions have been increasing remarkably in the past decades. In this paper, we propose a multi-organ lesion detection (MOLD) approach to better address real-life chest-related clinical needs. MOLD is a challenging task, especially within a large, high resolution image volume, due to various types of background information interference and large differences in lesion sizes. Furthermore, the appearance similarity between lesions and other normal tissues demands more discriminative features. In order to overcome these challenges, we introduce depth-aware (DA) and skipped-layer hierarchical training (SHT) mechanisms with the novel Dense 3D context enhanced (Dense 3DCE) lesion detection model. The novel Dense 3DCE framework considers the shallow, medium, and deep-level features together comprehensively. In addition, equipped with our SHT scheme, the backpropagation process can now be supervised under precise control, while the DA scheme can effectively incorporate depth domain knowledge into the scheme. Extensive experiments have been carried out on a publicly available, widely used DeepLesion dataset, and the results prove the effectiveness of our DA-SHT Dense 3DCE network in the MOLD task. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has emerged as a key instrument in oceanography due to its high spatial resolution and sensitivity to ocean surface dynamics. The main limitation of a single spaceborne SAR is the long repeat cycle (e.g., 12 days for Sentinel‐1), which hinders its capability to monitor the temporal evolution of oceanic processes. The principal objective of this study is to demonstrate the potential of spaceborne SAR to monitor the temporal variation of ocean surface circulation. This is assessed using the Baltic Sea flow through the Danish strait Fehmarn Belt as a case study. In order to overcome the temporal sampling limitation, data from three satellites are combined, namely Sentinel‐1A, Sentinel‐1B and TanDEM‐X. The average revisit time achieved by combining the three satellites is 1.2 days. Two months of opportunistic SAR data (June and July 2020) covering the Fehmarn Belt are used. The radial surface current derived from SAR is compared to ocean model and in situ data. It is shown that the dominant processes that govern the circulation in the Fehmarn Belt exhibit time scales larger than 2 days. Subsequently, it is demonstrated that SAR effectively captures the synoptic‐scale features (time scales larger than 2 days) of the Baltic Sea circulation, thereby enabling monitoring the temporal variations of flow dynamics. Comparison of the SAR‐derived radial surface current against in situ measurements yields comparable bias (≤ ${\le} $0.08 m/s) and correlation coefficient (R ≈ ${\approx} $ 0.75) but lower standard deviations and rms errors (0.15 m/s) than those exhibited by the ocean model (0.31 m/s). Plain Language Summary: Synthetic aperture radar (SAR) provides high resolution images of the ocean surface from space. These images contain information on the roughness of the ocean surface, which is related to wind speed and wave height, and information on the motion of the surface which is related to the wave propagation and surface currents. The ocean surface is dynamic. However, it changes rapidly in some regions and slowly in other regions. This depends on the time scale of the forces driving the circulation in that region. The main limitation of satellites for ocean observation is that they do not pass often over the same area. In this study, we investigate the time scale of the dominant driving processes like sea level and wind in the Danish straits area. This area contains important channels for water exchange between the Baltic Sea and the North Sea. Our investigation reveals that the dominant time scale governing forcing processes in this region exceeds 2 days. Furthermore, through the synergistic utilization of data from three satellite platforms, we demonstrate the capability to effectively resolve the surface flow dynamics of the Baltic Sea with a high degree of precision. Key Points: The dominant processes that govern the sea surface circulation in the Danish strait Fehmarn Belt exhibit time scales larger than 2 daysTo properly sample Baltic Sea time scales, data from three satellites are combined to achieve an average revisit time of 1.2 daysSAR is capable of monitoring the synoptic scale circulation of the Baltic Sea with adequate temporal resolution and high accuracy [ABSTRACT FROM AUTHOR]

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications. [ABSTRACT FROM AUTHOR]