Current Imaging Research Articles

The Future of Radar Space Observation in Europe—Major Upgrade of the Tracking and Imaging Radar (TIRA)

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34m Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe.

Universal non-circular cone beam CT orbits for metal artifact reduction imaging during image-guided procedures

Innovation in image-guided procedures has been driven by advances in robotic Cone Beam Computed Tomography (CBCT) systems. A fundamental challenge for CBCT imaging is metal artifacts arising from surgical tools and implanted hardware. Here, we outline how two universal non-circular imaging orbits, optimized for metal artifact reduction, can be implemented in real-time on clinical robotic CBCT systems. Demonstrating potential clinical utility, the universal orbits were implemented during a pedicle screw cervical spine fixation and hip arthroplasty performed on a porcine and ovine cadaver respectively. In both procedures, the universal non-circular orbits noticeably reduced the metal artifacts surrounding the implanted orthopedic hardware, revealing anatomy and soft tissue obscured in current conventional CBCT imaging. This work represents a key step in clinically translating universal orbits, unlocking high quality in-room procedural verification to increase broader use of robotic CBCT systems and reduce the occurrence of secondary corrective surgeries.

Evaluation of monocular and binocular contrast perception on virtual reality head-mounted displays.

Visualization of medical images on a virtual reality (VR) head-mounted display (HMD) requires binocular fusion of a stereoscopic pair of graphical views. However, current image quality assessment on VR HMDs for medical applications has been primarily limited to time-consuming monocular optical bench measurement on a single eyepiece. As an alternative to optical bench measurement to quantify the image quality on VR HMDs, we developed a WebXR test platform to perform contrast perceptual experiments that can be used for binocular image quality assessment. We obtained monocular and binocular contrast sensitivity responses (CSRs) from participants on a Meta Quest 2 VR HMD using varied interpupillary distance (IPD) configurations. The perceptual result shows that contrast perception on VR HMDs is primarily affected by optical aberration of the VR HMD. As a result, monocular CSR degrades at a high spatial frequency greater than 4 cycles per degree when gazing at the periphery of the display field of view, especially for mismatched IPD settings consistent with optical bench measurements. On the contrary, binocular contrast perception is dominated by the monocular view with superior image quality measured by the contrast. We developed a test platform to investigate monocular and binocular contrast perception by performing perceptual experiments. The test method can be used to evaluate monocular and/or binocular image quality on VR HMDs for potential medical applications without extensive optical bench measurements.

Temporal and chromatic variation of polarized scattered light in the outer disk of PDS 70

PDS 70 stands out as the only system hosting a protoplanetary disk and two confirmed planets undergoing formation. It is a unique target for characterizing the dust in this type of disk. We aim to accurately measure the reflected polarized intensity and quantify the variability and asymmetry for PDS 70 across multiple epochs and wavelengths in the optical and near-infrared. We present new high-contrast polarimetric differential imaging observations of PDS 70, with the $N\_R$ filter on SPHERE/ZIMPOL. We combined the new observation with archival data of the VLT/SPHERE instrument, spanning five wavelengths ($N\_R$, $VBB$, $J$, $H$, and $Ks$) over seven epochs and eight years. For each observational epoch, we corrected the smearing effect due to finite instrument resolution, measured the azimuthal brightness profiles, and derived the intrinsic disk-integrated polarized reflectivity and the intrinsic brightness contrasts. With our homogeneous analysis of all available optical and near-infrared data sets of the disk around PDS 70, we find significant temporal variability of the integrated polarized reflectivity as well as the azimuthal brightness profile. This indicates variable shadowing on the outer disk by inner disk structures beyond the resolution limit of current imaging instruments. Despite these variabilities, we observe a systematic wavelength-dependent contrast between the near side and the far side of the inclined disk. These results underline the importance of considering both the shadowing effect from the inner disk and the surface geometry of the observed reflecting disk in the analysis and interpretation of observational data.

A Brief Review of Hemp Fiber Length Measurement Techniques

Accurate fiber length measurement is essential for the processing and quality management of textile products. This article reviews the current methods used to measure fiber length, including manual, photoelectric, capacitive, and optical techniques. Existing sample preparation processes for natural fiber characterization have been primarily developed for cotton and wool fibers. However, hemp fibers present unique challenges due to their greater length variability, high strength, and low elongation, making some traditional sample preparation methods less effective. Image processing offers a promising approach for scalable and precise measurement of hemp fiber length. Nevertheless, current image processing techniques are limited by the inability to effectively handle overlapping fibers, which increases both the time and cost of testing. Continued research into developing more advanced segmentation algorithms could lead to more widely adopted commercial methods for fiber measurement.

Field cycling imaging to characterise breast cancer at low and ultra-low magnetic fields below 0.2 T

BackgroundThis prospective feasibility study explores Field-Cycling Imaging (FCI), a new MRI technology that measures the longitudinal relaxation time across a range of low magnetic field strengths, providing additional information about the molecular properties of tissues. This study aims to assess the performance of FCI and investigate new quantitative biomarkers at low fields within the context of breast cancer.MethodsWe conducted a study involving 9 people living with breast cancer (10 tumours in total, mean age, 54 ± 10 years). FCI images were obtained at four magnetic field strengths (2.3 mT to 200 mT). FCI images were processed to generate T1 maps and 1/T1 dispersion profiles from regions of tumour, normal adipose tissue, and glandular tissue. The dispersion profiles were subsequently fitted using a power law model. Statistical analysis focused on comparing potential FCI biomarkers using a Mann-Whitney U or Wilcoxon signed rank test.ResultsWe show that low magnetic fields clearly differentiate tumours from adipose and glandular tissues without contrast agents, particularly at 22 mT (1/T1, median [IQR]: 6.8 [3.9–7.8] s−1 vs 9.1 [8.9–10.2] s−1 vs 8.1 [6.2–9.2] s−1, P < 0.01), where the tumour-to-background contrast ratio was highest (62%). Additionally, 1/T1 dispersion indicated a potential to discriminate invasive from non-invasive cancers (median [IQR]: 0.05 [0.03–0.09] vs 0.19 [0.09–0.26], P = 0.038).ConclusionsTo the best of our knowledge, we described the first application of in vivo FCI in breast cancer, demonstrating relevant biomarkers that could complement diagnosis of current imaging modalities, non-invasively and without contrast agents.

Image Recognition Tools for Blind and Visually Impaired Users: An Emphasis on the Design Considerations

The existing digital image recognition technologies available for blind individuals are commercially accessible but still at an immature stage, necessitating enhancements in their capabilities. Areas requiring improvement in current image recognition tools for the visually impaired encompass information accuracy, information adequacy, appropriateness of information for blind individuals, functional sufficiency, and ergonomic suitability. This research endeavors to explore technology employing an inclusive approach to overcome limitations inherent in current digital image recognition technologies for the visually impaired. To streamline the research process, the initial phase involves a critical evaluation of deficiencies present in existing image recognition technologies for blind/visually impaired users through primary and secondary investigations. This strategic evaluation aims to identify key deficiencies, guiding assistive technology developers in focusing their efforts. Simultaneously, a survey is conducted to establish a comprehensive checklist of usability features and requirements for the proposed technology, aligning with the ISO 9241-110 standard. The overarching goal of this research is to address the question, "What design considerations should be taken into account in designing image recognition technology for blind and visually impaired people?" The research outcomes are intended to establish a standard for designers of digital products catering to blind/visually impaired users, fostering improved awareness and shaping attitudes toward individuals with visual disabilities in the development of image recognition software.

The aortic paradox: a nationwide analysis of 523,994 individual echocardiograms exploring aortic dissection

Abstract Background Increasing aortic dilation increases risk of aortic dissection. Nevertheless, dissection occurs below guideline-directed cut-offs for prophylactic surgery. There is no current large-scale population imaging data assessing aortic dimensions before dissection. Purpose To examine the proportion of aortic dissections occurring in people with aortic diameters below guidelines for prophylactic intervention. Methods Patients within the National Echo Database of Australia (NEDA) were stratified according to absolute, height-indexed and body surface area (BSA)-indexed aortic dimensions. Fatal thoracic aortic dissections (ICD-10-AM code I79) were identified via linkage with the National Death Index. Results 524,994 individuals were assessed, comprising patients with normal aortic dimensions (n=460,992), mild dilation (n=53,402), moderate dilation (n=10,029) and severe dilation (n=572). 274,992 (52.4%) were male, with median age 64 years and median follow-up time 6.9 years. 899 fatal aortic dissections occurred (normal diameter=610, mildly-dilated aorta=215, moderately-dilated =53 and severely-dilated=21). Using normal aortas as the reference population, odds of fatal dissection increased with aortic diameter (mild=OR 3.05, 95% confidence interval (CI) 2.61-3.56; moderate=OR 4.0, 95% CI 3.02-5.30; severe=OR 28.72, 95% CI 18.44-44.72). Due to the much larger number of patients without severe aortic dilation, 97.7% of fatal aortic dissections occurred in non-severely dilated aortas. Following sensitivity analysis, severe aortic dilation was responsible for at most 24.4% of fatal aortic dissections. Results were robust for absolute, height-indexed or BSA-indexed aortic measurements. Conclusion Although severe aortic dilatation is associated with a thirty-fold increase in fatal dissection, severely dilated aortas are implicated in only 2.3-24.4% of fatal dissections. This highlights the ‘aortic paradox’ and limitations of current guidelines. Future studies should seek to refine risk predictors in patients without severe aortic dilation.

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Quantitative molecular imaging of cardiac fibrosis and response to treatment using a novel collagen III-specific MR imaging probe

Abstract Background Heart failure (HF) remains a major cause of morbidity and death affecting 64 million people globally[1]. Myocardial fibrosis, characterised by changes in type I (COL1) and III (COL3) collagen levels, may lead to HF [2,3]. Cardiac magnetic resonance (CMR) is a preferred method to detect fibrosis non-invasively, however it provides only indirect measures of fibrosis. Molecular imaging can directly visualise fibrosis, but current imaging probes target COL1. Purpose Here, we developed a COL3-specific probe and used molecular CMR imaging to directly quantify, previously undetectable, COL3 remodelling following myocardial infarction and to monitor treatment response. Methods Myocardial infarction (MI) was induced in mice by permanent ligation of the left anterior descending artery (LAD). Functional and molecular CMR images were acquired at days 10 and 21 post-MI in untreated and mice treated with Enalapril (20mg/kg/day) (n=6/group) using a 3T clinical MRI scanner. A subgroup of untreated mice was also imaged using a negative control probe and Gadovist (n=3). Cardiac function was assessed using 2D short-axis cine images of the left ventricle, and late gadolinium enhancement (LGE) images were obtained using T1-weighted 3D inversion recovery (IR) imaging. T1 mapping was performed using a 2D Look-Locker sequence with thirty inversion recovery images. Results Molecular CMR enabled selective profiling of the natural turnover of COL3 after MI with strong signal enhancement at day 10, and decreased enhancement at day 21 when COL3 is replaced by COL1 (Fig. 1A). Quantitative T1 maps discriminated between infarcted and remote myocardium with lower T1 values in the infarct and higher in the remote myocardium. Importantly, there was no enhancement using the scrambled probe or the clinical agent Gadovist. The imaging data were validated by histology (Fig.1B). Mice treated with Enalapril showed similar enhancement at day 10 compared to untreated mice (Fig. 2A). However, at day 21 mice treated with Enalapril showed higher signal enhancement compared to untreated mice, suggesting that Enalapril may prolong the presence of COL3 in the infarcted myocardium. Quantification of signal enhancement showed the increase in LGE volume and R1 relaxation rates within the infarcted myocardium at day 10, the subsequent decrease at day 21 and the prolongation of the COL3 signal at day 21 in enalapril treated mice (Fig.2B-C) Despite changes in cardiac fibrosis detected with molecular imaging, cardiac function was similar between the groups (Fig. 2D), suggesting that molecular change may precede functional changes. Conclusion(s) Quantitative molecular CMR imaging of COL3 enabled visualisation of changes in COL3 after MI and in response to treatment for the first time. This approach may provide insights into the role of COL3 in cardiac fibrosis and provide an non-invasive method to detect fibrosis early, stage fibrosis and monitor therapeutic response.

Arthroscopy has a higher discriminative capacity than MRI in detecting mild cartilage lesions.

To determine whether current magnetic resonance imaging (MRI) could detect superficial cartilage lesions that are observed during diagnostic arthroscopy in patients with knee pain who have not been diagnosed with joint disease. Adult patients with knee pain of unclear origin lasting more than three months, scheduled for a therapeutic/diagnostic arthroscopy were recruited. Demographic and clinical data, pain assessment, MRI imaging, and observations of cartilage damage in the medial femoral condyle during arthroscopic procedure were documented. Patients were categorized based on the presence of cartilage damage assessed via MRI and/or direct visualization. Concordance between these assessments and its variation with age and patient-reported pain were examined. Out of the 95 patients recruited, 48 exhibited lesions in the medial femoral condyle (MFC) during arthroscopic examination, while only 24 of them showed lesions on the MRI scans. The thickness of the cartilage in the MFC was significantly lower in patients with cartilage damage detected by MRI compared with those without. Among patients with cartilage lesions identified during arthroscopy, those also showing lesions on the MRI had lower cartilage thickness and higher Outerbridge score than those without lesions on the MRI. Patients with detectable cartilage damage on the MRI were significantly older and reported higher levels of pain than those with damage detected only by arthroscopic examination. Despite significant technological advancements in MRI, arthroscopy still proves superior in identifying mild structural cartilage lesions that are not identifiable by this technique.

An algorithm based on multi-branch feature cross fusion for archaeological illustration of murals

Archaeological illustration is a graphic recording technique that delineates the shape, structure, and ornamentation of cultural artifacts using lines, serving as vital material in archaeological work and scholarly research. Aiming at the problems of low line accuracy in the results of current mainstream image generation algorithms and interference caused by severe mural damage, this paper proposes a mural archaeological illustration generation algorithm based on multi-branch feature cross fusion (U2FGAN). The algorithm optimizes skip connections in U2Net through a channel attention mechanism, constructing a multi-branch generator consisting of a line extractor and an edge detector, which separately identify line features and edge information in artifact images before fusing them to generate accurate, high-resolution illustrations. Additionally, a multi-scale conditional discriminator is incorporated to guide the generator in outputting high-quality illustrations with clear details and intact structures. Experiments conducted on the Dunhuang mural illustration datasets demonstrate that compared to mainstream counterparts, U2FGAN reduced the Mean Absolute Error (MAE) by 10.8% to 26.2%, while also showing substantial improvements in Precision (by 9.8% to 32.3%), Fβ-Score (by 5.1% to 32%), and PSNR (by 0.4 to 2.2 dB). The experimental results show that the proposed method outperforms other mainstream algorithms in archaeological illustration generation.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

Improved Localization of Insulinomas Using 68Ga-NODAGA-Exendin-4 PET/CT.

Precise anatomic localization of insulinomas is crucial for surgical treatment. Current routine noninvasive imaging techniques, including CT, MRI, and 68Ga-DOTA-somatostatin analog (DOTA-SSA) PET/CT, have limited sensitivity. Endoscopic ultrasound is highly sensitive but invasive. In this prospective multicenter study, we compared the diagnostic accuracy of 68Ga-NODAGA-exendin-4 (exendin) PET/CT with all routine imaging procedures for the localization of insulinomas. Methods: Sixty-nine adults with biochemically proven adult endogenous hyperinsulinemic hypoglycemia underwent exendin PET/CT and current routine imaging. Images were evaluated in a clinical reading and in an expert reading. Image quality was determined by quantitative analysis. Results: Based on clinical readings, the accuracy of exendin PET/CT (94.4%; 95% CI, 84.6%-98.8%) was greater than that of DOTA-SSA PET/CT (64.8%; 95% CI, 50.6%-77.3%), contrast-enhanced CT/contrast-enhanced diffusion-weighted imaging-MRI (83.3%; 95% CI, 70.7%-92.1%), and endoscopic ultrasound (82.8%; 95% CI, 64.1%-94.1%). In 13% of patients, a correct diagnosis was only reached after exendin PET/CT. Interobserver agreement between readings was higher for exendin PET/CT than for DOTA-SSA PET/CT and contrast-enhanced CT/contrast-enhanced diffusion-weighted imaging-MRI (Cohen κ, 1.0 vs. 0.5 and 0.55). Exendin PET/CT provided a higher insulinoma-to-background ratio (15.3 ± 6.7 vs. 5.2 ± 3.0) and contrast-to-noise ratio (22.6 ± 11.1 vs. 5.1 ± 3.7) than did DOTA-SSA PET/CT. Conclusion: This study demonstrates the superiority of exendin PET/CT in a unique prospective comparison to all current routine imaging modalities for preoperative localization of benign insulinomas, providing the level of evidence needed for clinical implementation.

Multi-Modal Graph Aggregation Transformer for image captioning

The current image captioning directly encodes the detected target area and recognizes the objects in the image to correctly describe the image. However, it is unreliable to make full use of regional features because they cannot convey contextual information, such as the relationship between objects and the lack of object predicate level semantics. An effective model should contain multiple modes and explore their interactions to help understand the image. Therefore, we introduce the Multi-Modal Graph Aggregation Transformer (MMGAT), which uses the information of various image modes to fill this gap. It first represents an image as a graph consisting of three sub-graphs, depicting context grid, region, and semantic text modalities respectively. Then, we introduce three aggregators that guide message passing from one graph to another to exploit context in different modalities, so as to refine the features of nodes. The updated nodes have better features for image captioning. We show significant performance scores of 144.6% CIDEr on MS-COCO and 80.3% CIDEr on Flickr30k compared to state of the arts, and conduct a rigorous analysis to demonstrate the importance of each part of our design.

Eosinophilic gastroenteritis, a rare case report and literature review

Abstract Introduction/Objective Eosinophilic gastroenteritis (EGE) is a rare inflammatory condition affecting the gastrointestinal tract, initially described by Kaijser et al. in 1937. It manifests as eosinophil-rich infiltration throughout the GI tract, often accompanied by peripheral eosinophilia. EGE is categorized under eosinophilic gastrointestinal disorders (EGIDs), alongside eosinophilic esophagitis (EOE) and eosinophilic colitis (EC). Symptoms vary based on the affected GI segment. This report aims to present a case of extensive EGE in a young male patient and review of similar cases to enhance clinical and histopathologic understanding. Methods/Case Report A 29-year-old male presented with abdominal pain, nausea, vomiting, and recent weight loss. He had a history of similar abdominal symptoms for a few years. Previous extensive investigations were inconclusive. Current imaging revealed soft tissue nodules, mild splenomegaly, and biliary dilation. Laboratory tests showed no significant abnormalities. Endoscopy revealed a gastric antral ulcer with marked eosinophilic infiltration on microscopy. Results (if a Case Study enter NA) NA Conclusion Eosinophilic gastroenteritis is an uncommon disease that presents with variable non-specific symptoms which require a high level of suspicion for diagnosis. EGE diagnosis requires excluding other causes of eosinophil accumulation. There is no clear distinction between the upper normal and abnormally increased eosinophils in GIT biopsies. Peripheral eosinophilia is common, along with other abnormal laboratory findings. Diagnosis relies on histopathology due to nonspecific symptoms. Clinicians and pathologists face challenges in diagnosing EGE, emphasizing the importance of considering it in the differential diagnoses. Efforts to establish diagnostic criteria for EGE are limited, despite its long-known existence. The condition’s vague symptoms necessitate a high level of clinical suspicion, with diagnosis primarily based on histopathologic features.

Hyperspectral oblique plane microscopy enables spontaneous, label-free imaging of biological dynamic processes in live animals

Spontaneous Raman imaging has emerged as powerful label-free technique for investigating the molecular composition of medicines and biological specimens. Although Raman imaging can facilitate understanding of complex biological phenomena in vivo, current imaging modalities are limited in speed and sample compatibility. Here, we introduce a single-objective line-scanning light-sheet microscope, named [Formula: see text]-OPM, which records Raman images on a timescale of minutes to seconds. To demonstrate its function, we use [Formula: see text]-OPM to map and identify microplastic particles based on their Raman spectral characteristics. In live zebrafish embryos, we show that [Formula: see text]-OPM can capture wound dynamics at five-minute intervals, revealing rapid changes in cellular and extracellular matrix composition in the wounded region. Finally, we use [Formula: see text]-OPM to synchronize and average 36,800 individual frames to obtain hyperspectral videos of a zebrafish embryo's beating heart at an effective 28 frames per second, recording compositional changes throughout the cardiac cycle.

Dehazing network based on residual context attention and cross-layer fusion

Current image dehazing algorithms based on deep learning usually use traditional convolutional layers when extracting features, which easily causes the loss of image details and edge information, ignores the position information of the image when extracting features, and ignores the original information of the image when fusing features, and cannot restore high-quality haze-free images with complete structure and clarity. To address this problem, a dehazing algorithm based on residual context attention and cross-layer feature fusion is proposed. Firstly, the residual group structure is obtained by serializing the proposed residual context blocks, and the features of the first two layers of the network, i.e., the shallow layers, are extracted to obtain rich context information of the shallow layers; secondly, coordinate attention is introduced to establish an attention map with position information, and it is applied to the residual context feature extraction and placed in the third layer of the network, i.e., the deep layer, to extract deeper semantic information; then, in the middle layer of the network, feature information from different resolution streams is fused across layers to enhance the information exchange between the shallow and deep layers to achieve the purpose of feature enhancement; finally, the features with rich semantic information obtained by the network are aggregated with the original input, thereby improving the restoration effect. Experimental results on the RESIDE dataset and the Haze4K dataset show that the proposed algorithm achieves good results in both visual effects and objective indicators.

Image Restoration of Electrical Well Logging Based on Fourier Convolution

Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.

A Geometric View of Seismic Wavefields: Implications for Imaging Dense Clusters of Events

Summary Imaging dense clusters of seismicity is crucial to many problems in seismology: to delineate complex systems of faults, provide constraints on the causes of volcanic and cryogenic swarms, and to shed light on possible means to prevent damaging induced seismicity in mining, geothermal and oil and gas extraction activities. Current imaging methods rely upon high-resolution relative location techniques, commonly requiring arrival-time picks for seismic phases. This paper examines an alternative approach, based upon concepts drawn from differential geometry, that images directly from waveform data. It relies upon the common assumption of spatial continuity of seismic wavefield observations, which implies that a differentiable map exists between the source region to be imaged and waveform observations considered as elements of a vector space. The map creates an image of event clusters on a Riemannian manifold embedded in that vector space. The image can be visualized by projecting the observations into a tangent space of the manifold and is a distorted rendering of cluster geometry. However, the distortion can be predicted and removed if a model for wavefield propagation is available. This visualization approach is applicable to clusters of uniform events with highly similar waveforms, such as are commonly acquired with correlation detectors or other pattern matching techniques. To assess its performance, it is applied to the closely-related reciprocal problem of imaging the (known) geometry of an array from observations by the array of several regional events. Differences between the original problem and its reciprocal analog are noted and controlled for in the analysis. Chief among the differences is the necessity for aligning the waveforms in the original problem, which, to maintain consistency with the original problem, is solved in the reciprocal problem by a generalization of the VanDecar-Crosson algorithm. The VanDecar-Crosson algorithm exhibits a bias, shown through an analysis of the situation when the observed wavefields are adequately modeled as plane waves. In that circumstance, the bias can be predicted and removed. In a test using a portion of a large-N array, this imaging approach is shown to successfully reconstruct the array geometry. The method is applicable directly to infinitesimal array apertures, but is extended to a larger aperture by partitioning the image into local, effectively infinitesimal overlapping subsets. These are inverted, then assembled into a global picture of the array geometry using constraints provided by the overlapped regions. Although demonstrated in a reciprocal array context, the method appears viable for imaging clusters of events with highly similar source mechanisms and time histories.