Limited Field Of View Research Articles

High-quality manipulable fiber-microsphere for super-resolution microscopy.

Despite the gain in resolution brought by microsphere (MS)-assisted microscopy, it has always faced several limitations, such as a limited field of view, surface defects, low contrast, and lack of manipulability. This Letter presents a new type of MS created at the tip of an optical fiber, which we call a fiber microsphere (fMS). The fMS is made from a single-mode or coreless fiber, molten and stretched, ensuring high homogeneity and a sphere diameter smaller than the fiber itself. In addition, the connection between the fMS and the fiber makes scanning the sample a simple task, offering a solution to the difficulties of handling. The fabrication procedure of the fMS and the optical system used in the study are detailed. Our measurements show a clear superiority of the fMS over the soda-lime MS in resolving power and imaging performance.

How useful is pre-referral pediatric spine imaging?

PurposePrimary care physicians rely on radiology reports to confirm a scoliosis diagnosis and inform the need for spine specialist referral. In turn, spine specialists use these reports for triage decisions and planning of care. The objectives were to determine (1) the adequacy of index images to inform treatment decisions at the initial consultation and (2) the utility of index radiology reports for appropriate triage decisions.MethodsA retrospective chart review was conducted including all idiopathic scoliosis patients seen for initial consultation, aged three to 18 years, between January 1 and April 30, 2021. A score out of five was generated based on the adequacy of index images to provide accurate Cobb angle measurements and determine skeletal maturity. Index images were considered inadequate if repeat imaging was necessary. Index radiology reports, associated imaging, and new imaging, if obtained at the initial consultation, were compared.ResultsOf the 94 patients reviewed, 79% (n = 74) required repeat imaging at the initial consultation, of which 74% (n = 55) were due to insufficient quality and/or limited field of view. Of index images available for review at the initial consult (n = 80), 41.2% scored five out of five, and 32.5% scored two or below. Comparing index radiology reports to initial visit evaluation with ≤ 60 days between imaging (n = 49), discrepancies in Cobb angle were found in 24.5% (95% CI 14.6, 38.1) of patients. The Risser stage was reported in 14% of index radiology reports.ConclusionAlthough pre-referral pediatric spine radiographs serve a diagnostic purpose, most are inadequate for comprehensive idiopathic scoliosis evaluation.Level of evidenceIII.

A Cross-View Image Matching Method with Feature Enhancement

Most cross-view image matching algorithms focus on designing network structures with excellent performance, ignoring the content information of the image. At the same time, there are non-fixed targets such as cars, ships, and pedestrians in ground perspective images and aerial perspective images. Differences in perspective, direction, and scale cause serious interference with the cross-view matching process. This paper proposes a cross-view image matching method with feature enhancement, which first transforms the empty image to generate a transformation image aligned with the ground–aerial image domain to establish a preliminary geometric correspondence between the ground-space image. Then, the rich feature information of the deep network and the edge information of the cross-convolution layer are used to establish the feature correspondence between the ground-space images. The feature fusion module enhances the tolerance of the network model to scale differences, improving the interference problem of transient non-fixed targets on the matching performance in the images. Finally, the maximum pooling and feature aggregation strategies are adopted to aggregate local features with obvious distinguishability into global features to complete the accurate matching between ground images. The experimental results show that the proposed method has good advance and high accuracy on CVUSA, which is commonly used in public datasets, reaching 92.23%, 98.47%, and 99.74% on the top 1, top 5 and top 10 indicators, respectively, outperforming the original method in the dataset with a limited field of view and image center, better completing the cross-perspective image matching task.

Triangular lattice formation in robot swarms with minimal local sensing

AbstractThe problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi‐robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi‐robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots.

Practical considerations for determination of scapular internal rotation and its relevance in reverse total shoulder arthroplasty planning

BackgroundScapulothoracic orientation, especially scapular internal rotation (SIR) may influence range of motion in reverse total shoulder arthroplasty (RTSA) and is subjected to body posture. Clinical measurements of SIR rely on apical bony landmarks, which depend on changes in scapulothoracic orientation, while radiographic measurements are often limited by the restricted field of view (FOV) in CT scans. Therefore, the goal of this study was (1) to determine whether the use of CT scans with a limited FOV to measure SIR is reliable and (2) if a clinical measurement could be a valuable alternative.MethodsThis anatomical study analyzed the whole-body CT scans of 100 shoulders in 50 patients (32 male and 18 female) with a mean age of 61.2 ± 20.1 years (range 18; 91). (1) CT scans were rendered into 3D models and SIR was determined as previously described. Results were compared to measurements taken in 2D CT scans with a limited FOV. (2) Three apical bony landmarks were defined: (the angulus acromii (AA), the midpoint between the AA and the coracoid process tip (C) and the acromioclavicular (AC) joint. The scapular axis was determined connecting the trigonum scapulae with these landmarks and referenced to the glenoid center. The measurements were repeated with 0°, 10°, 20°, 30° and 40° anterior scapular tilt.ResultsMean SIR was 44.8° ± 5.9° and 45.6° ± 6.6° in the 3D and 2D model, respectively (p < 0.371). Mean difference between the measurements was 0.8° ± 2.5° with a maximum of 10.5°. Midpoint AA/C showed no significant difference to the scapular axis at 0° (p = 0.203) as did the AC-joint at 10° anterior scapular tilt (p = 0.949). All other points showed a significant difference from the scapular axis at all degrees of tilt.Conclusion2D CT scans are reliable to determine SIR, even if the spine is not depicted. Clinical measurements using apical superficial scapula landmarks are a possible alternative; however, anterior tilt influenced by posture alters measured SIR.

Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation.

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice computed tomography (CT) provides a high resolution, quantitative tissue map, albeit with a limited field of view. Although numerous potential analyses have been proposed in quantifying image context, there has been no comprehensive study for low-dose single slice CT longitudinal variability with automated segmentation. We studied a total of 1816 slices from 1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset using supervised deep learning-based segmentation and unsupervised clustering method. 300 out of 1469 subjects that have two year gap in their first two scans were pick out to evaluate longitudinal variability with measurements including intraclass correlation coefficient (ICC) and coefficient of variation (CV) in terms of tissues/organs size and mean intensity. We showed that our segmentation methods are stable in longitudinal settings with Dice ranged from 0.821 to 0.962 for thirteen target abdominal tissues structures. We observed high variability in most organ with ICC<0.5, low variability in the area of muscle, abdominal wall, fat and body mask with average ICC≥0.8. We found that the variability in organ is highly related to the cross-sectional position of the 2D slice. Our efforts pave quantitative exploration and quality control to reduce uncertainties in longitudinal analysis.

High-throughput single-molecule localization microscopy: Potential clinical applications.

High-throughput single-molecule localization microscopy: Potential clinical applications.

Ultrastructural Localization of Endogenous LC3 by On-Section Correlative Light-Electron Microscopy.

The visualization of autophagic organelles at the ultrastructural level by electron microscopy (EM) is essential to establish their identity and reveal details that are important for understanding the autophagic process. However, EM methods often lack molecular information, obstructing the correlation of ultrastructural information obtained by EM to fluorescence microscopy-based localization of specific autophagy proteins. Furthermore, the rarity of autophagosomes in unaltered cellular conditions hampers investigation by EM, which requires high magnification, and hence provides a limited field of view. In answer to both challenges, an on-section correlative light-electron microscopy (CLEM) method based on fluorescent labeling was applied to correlate a common autophagosomal marker, LC3, to EM ultrastructure. The method was used to rapidly screen cells in fluorescence microscopy for LC3 labeling in combination with other relevant markers. Subsequently, the underlying ultrastructural features of selected LC3-labeled spots were identified by CLEM. The method was applied to starved cells without adding inhibitors of lysosomal acidification. In these conditions, LC3 was found predominantly on autophagosomes and rarely in autolysosomes, in which LC3 is rapidly degraded. These data show both the feasibility and sensitivity of this approach, demonstrating that CLEM can be used to provide ultrastructural insights on LC3-mediated autophagy in native conditions-without drug treatments or genetic alterations. Overall, this method presents a valuable tool for ultrastructural localization studies of autophagy proteins and other scarce antigens by bridging light microscopy to EM data.

Optically Non-Contact Cross-Country Skiing Action Recognition Based on Key-Point Collaborative Estimation and Motion Feature Extraction.

Technical motion recognition in cross-country skiing can effectively help athletes to improve their skiing movements and optimize their skiing strategies. The non-contact acquisition method of the visual sensor has a bright future in ski training. The changing posture of the athletes, the environment of the ski resort, and the limited field of view have posed great challenges for motion recognition. To improve the applicability of monocular optical sensor-based motion recognition in skiing, we propose a monocular posture detection method based on cooperative detection and feature extraction. Our method uses four feature layers of different sizes to simultaneously detect human posture and key points and takes the position deviation loss and rotation compensation loss of key points as the loss function to implement the three-dimensional estimation of key points. Then, according to the typical characteristics of cross-country skiing movement stages and major sub-movements, the key points are divided and the features are extracted to implement the ski movement recognition. The experimental results show that our method is 90% accurate for cross-country skiing movements, which is equivalent to the recognition method based on wearable sensors. Therefore, our algorithm has application value in the scientific training of cross-country skiing.

Propagation of coronal mass ejections from the Sun to the Earth

Coronal Mass Ejections (CMEs), as they can inject a large amounts of mass and magnetic flux into the interplanetary space, are the primary source of space weather phenomena on the Earth. The present review first briefly introduces the solar surface signatures of the origins of CMEs and then focuses on the attempts to understand the kinematic evolution of CMEs from the Sun to the Earth. CMEs have been observed in the solar corona in white-light from a series of space missions over the last five decades. In particular, LASCO/SOHO has provided almost continuous coverage of CMEs for more than two solar cycles until today. However, the observations from LASCO suffered from projection effects and limited field of view (within 30 Rs from the Sun). The launch in 2006 of the twin STEREO spacecraft made possible multiple viewpoints imaging observations, which enabled us to assess the projection effects on CMEs. Moreover, heliospheric imagers (HIs) onboard STEREO continuously observed the large and unexplored distance gap between the Sun and Earth. Finally, the Earth-directed CMEs that before have been routinely identified only near the Earth at 1 AU in in situ observations from ACE and WIND, could also be identified at longitudes away from the Sun-Earth line using the in situ instruments onboard STEREO. Our review presents the frequently used methods for estimation of the kinematics of CMEs and their arrival time at 1 AU using primarily SOHO and STEREO observations. We emphasize the need of deriving the three-dimensional (3D) properties of Earth-directed CMEs from the locations away from the Sun-Earth line. The results improving the CME arrival time prediction at Earth and the open issues holding back progress are also discussed. Finally, we summarize the importance of heliospheric imaging and discuss the path forward to achieve improved space weather forecasting.

Development of Virtual Reality Walking Collision Detection Test on Head-mounted display.

Detecting and avoiding collisions during walking is critical for safe mobility. To determine the effectiveness of clinical interventions, a realistic objective outcome measure is needed. A real-world obstacle course with moving hazards has numerous limitations (e.g., safety concerns of physical collision, inability to control events, maintaining event consistency, and event randomization). Virtual reality (VR) platforms may overcome such limitations. We developed a VR walking collision detection test using a standalone head-mounted display (HMD, Meta Quest 2) with the Unity 3D engine to enable subjects' physical walking within a VR environment (i.e., a busy shopping mall). The performance measures focus on the detection and avoidance of potential collisions, where a pedestrian may (or may not) walks toward a collision with the subject, while various non-colliding pedestrians are presented simultaneously. The physical space required for the system was minimized. During the development, we addressed expected and unexpected hurdles, such as mismatch of visual perception of VR space, limited field of view (FOV) afforded by the HMD, design of pedestrian paths, design of the subject task, handling of subject's response (detection or avoidance behavior), use of mixed reality (MR) for walking path calibration. We report the initial implementation of the HMD VR walking collision detection and avoidance scenarios that showed promising potential as clinical outcome measures.

A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy

Catheterization is a procedure used to diagnose and treat various cardiovascular diseases. Intracardiac echocardiography (ICE) is an emerging imaging modality that has gained popularity in these procedures due to its ability to provide high-resolution images of the heart and its surrounding structures in a minimally invasive manner. However, given its limited field of view, its orientation within the heart is difficult to judge simply from observing the acquired images. Therefore, ICE catheter tracking, which requires six degrees of freedom, would be useful to better guide interventionalists during a procedure. This work demonstrates a machine learning-based approach that has been trained to predict the roll angle of an ICE catheter using landmark scalar values extracted from bi-plane fluoroscopy images. The model consists of two fully connected deep neural networks that were trained on a dataset of bi-plane fluoroscopy images acquired from a 3D printed heart phantom. The results showed high accuracy in roll angle prediction, suggesting the ability to achieve 6 degrees of freedom tracking using bi-plane fluoroscopy that can be integrated into future navigation systems embedded into the c-arm, integrated within an AR/MR headset, or in other commercial navigation systems.

A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton–Raphson-Based Feature Selection Approach for Human Gait Recognition

The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging covariant factors that degrade gait recognition performance include walking while wearing a coat or carrying a bag. This paper proposed a new two-stream deep learning framework for human gait recognition. The first step proposed a contrast enhancement technique based on the local and global filters information fusion. The high-boost operation is finally applied to highlight the human region in a video frame. Data augmentation is performed in the second step to increase the dimension of the preprocessed dataset (CASIA-B). In the third step, two pre-trained deep learning models-MobilenetV2 and ShuffleNet-are fine-tuned and trained on the augmented dataset using deep transfer learning. Features are extracted from the global average pooling layer instead of the fully connected layer. In the fourth step, extracted features of both streams are fused using a serial-based approach and further refined in the fifth step by using an improved equilibrium state optimization-controlled Newton-Raphson (ESOcNR) selection method. The selected features are finally classified using machine learning algorithms for the final classification accuracy. The experimental process was conducted on 8 angles of the CASIA-B dataset and obtained an accuracy of 97.3, 98.6, 97.7, 96.5, 92.9, 93.7, 94.7, and 91.2%, respectively. Comparisons were conducted with state-of-the-art (SOTA) techniques, and showed improved accuracy and reduced computational time.

State of the Art of Molecular Visualization in Immersive Virtual Environments

AbstractVisualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.

A monocular wide-field speed sensor inspired by the crabs’ visual system for traffic analysis

The development of visual sensors for traffic analysis can benefit from mimicking two fundamental aspects of the visual system of crabs: their panoramic vision and their visual processing strategy adapted to a flat world. First, the use of omnidirectional cameras in urban environments allows for analyzing the simultaneous movement of many objects of interest over broad areas. This would reduce the costs and complications associated with infrastructure: installation, synchronization, maintenance, and operation of traditional vision systems that use multiple cameras with a limited field of view. Second, in urban traffic analysis, the objects of interest (e.g. vehicles and pedestrians) move on the ground surface. This constraint allows the calculation of the 3D trajectory of the vehicles using a single camera without the need to use binocular vision techniques.The main contribution of this work is to show that the strategy used by crabs to visually analyze their habitat (monocular omnidirectional vision with the assumption of a flat world ) is useful for developing a simple and effective method to estimate the speed of vehicles on long trajectories in urban environments. It is shown that the proposed method estimates the speed with a root mean squared error of 2.7 km h−1.

Magneto-thermal-acoustic imaging of magnetic nanoparticles with tissue structure imaging function

Magneto-thermal-acoustic imaging with magnetic nanoparticles (MNPs) is a novel medical imaging modality based on the thermoacoustic (TA) effect of MNPs. We proposed a new dual-modality imaging strategy that combines the TA and echo signals. Based on the synthetic aperture method, we used TA signals to image cancer tissues labeled with MNPs and applied echo signals to achieve tissue structure imaging. In the simulation, we constructed a breast cancer model and assumed that cancer tissue was labeled with MNPs. Under a limited field of view, the TA and echo signals were collected using a multi-element transducer array. By fusing the imaging results of the TA and echo signals, we obtained a boundary distribution map of the breast cancer model, and accurately located the tumor tissue. In this experiment, we built a multi-channel acquisition system. Pork tissue labeled with MNPs was used as the breast cancer phantom. We collected a total of 120 TA and echo signals at 1 mm intervals using a multi-element transducer array and faithfully imaged the boundary distribution map of the phantom. Compared with the traditional linear back projection method based on focused transducers, we demonstrated that the proposed method not only improves the lateral resolution of the image, but also makes accurate cancer identification easier. This provides a promising medical imaging technology for clinical applications.

Tandem Observations of Nighttime Mid‐Latitude Topside Ionospheric Perturbations

AbstractNighttime medium‐scale traveling ionospheric disturbances (MSTIDs) have been generally observed by ground‐based instruments. However, they provide 2‐dimensional images over only a limited field of view and are not distributed globally. The ground‐based observations reported that MSTID wavefronts exhibit backward‐C shapes virtually straddling the dip equator. In situ plasma density measurements onboard individual satellites could overcome the limited coverage of ground‐based MSTID observations. But, most of those spacecrafts could obtain only 1‐dimensional profiles of plasma density, which leaves uncertain whether the observed perturbations generally have the characteristic directivity of MSTIDs. This paper addresses this knowledge gap by statistically investigating nighttime perturbations in the mid‐latitude topside ionosphere observed by tandem satellites, Swarm A and C. We cross‐correlate the plasma density profiles observed by Swarm A and C. The correlation coefficient tends to increase as the two spacecraft move closer, allowing us to derive the disturbances' directivity whenever the Swarm A and C observations are correlated significantly. The directivity statistics agree well with the backward‐C shape. Furthermore, the wavefront directions have clear dependence on magnetic latitudes while they are not as well aligned with local time, which is also consistent with previous reports on nighttime MSTIDs using ground‐based observations and computer simulations. Additionally, we demonstrate that the nighttime MSTIDs can increase the topside Rate Of Total electron content Index above Swarm. All the above‐mentioned results support that the nighttime mid‐latitude perturbations observed by Swarm can be identified as MSTIDs on the whole, which is the most important finding of this paper.

Comparison of nanocomposite dispersion and distribution for several melt mixers

Breakup (dispersion) and distribution of nanoparticles are the chief hurdles towards taking advantage of nanoparticles in polymer nanocomposites for reinforcement, flame retardancy, conductivity, chromaticity, and other properties. Microscopy is often used to quantify mixing, but it has a limited field of view, does not average over bulk samples, and fails to address nano-particle hierarchical structures. Ultra-small-angle X-ray scattering (USAXS) can provide a macroscopic statistical average of nanoscale dispersion (breakup) and emergent hierarchical structure, as well as the distribution on the nanoscale. This work compares several common mixer geometries for carbon black-polystyrene nanocomposites. Two twin-screw extruder geometries, typical for industrial processing of melt blends, are compared with a laboratory-scale single screw extruder and a Banbury mixer. It is found that for a given mixer, nanoscale distribution increases following a van der Waals function using accumulated strain as an analogue for temperature while macroscopic distribution/dispersion, using microscopy, does not follow this dependency. Breakup and aggregation in dispersive mixing follow expected behavior on the nanoscale. Across these drastically different mixing geometries an unexpected dependency is observed for nanoscale distributive mixing (both nano and macroscopic) as a function of accumulated strain that may reflect a transition from distributive turbulent to dispersive laminar mixing as the mixing gap is reduced.

Platoon formation control of autonomous underwater vehicles under LOS range and orientation angles constraints

A new controller design is presented on the basis of the dynamic surface control (DSC) method for the constrained platoon formation control of the underactuated autonomous underwater vehicles (AUVs) in the presence of model uncertainties with limited field-of-view (FOV) constraints. An asymmetric barrier Lyapunov function (BLF) is applied to prevent both the line-of-sight (LOS) range and orientation angles from violating limited FOV constraints. Lyapunov method is adopted to reveal that all signals of the closed-loop control system are bounded and the constrained platoon formation tracking errors are semi-globally uniformly ultimately bounded (SGUUB). An adaptive neural network technique is employed to compensate uncertain parameters and unmodeled dynamics of the vehicles. Compared with existing works in the literature, the proposed control scheme not only forces both the range and orientation angles between AUVs to their desired values in the presence of FOV constraints in the fixed-time, but also decrease the safety hazard by avoiding any possible collision between the vehicles, increase the tracking error convergence rate and improve the transient performance of the tracking controller for all AUVs. The computer simulation results demonstrate the efficiency of this newly proposed constrained platoon formation controller for the AUVs.

A Wide Energy Range and 4π-View Gamma Camera with Interspaced Position-Sensitive Scintillator Array and Embedded Heavy Metal Bars.

(1) Background: Gamma cameras have wide applications in industry, including nuclear power plant monitoring, emergency response, and homeland security. The desirable properties of a gamma camera include small weight, good resolution, large field of view (FOV), and wide imageable source energy range. Compton cameras can have a 4π FOV but have limited sensitivity at low energy. Coded-aperture gamma cameras are operatable at a wide photon energy range but typically have a limited FOV and increased weight due to the thick heavy metal collimators and shielding. In our lab, we previously proposed a 4π-view gamma imaging approach with a 3D position-sensitive detector, with which each detector element acts as the collimator for other detector elements. We presented promising imaging performance for 99mTc, 18F, and 137Cs sources. However, the imaging performance for middle- and high-energy sources requires further improvement. (2) Methods: In this study, we present a new gamma camera design to achieve satisfactory imaging performance in a wide gamma energy range. The proposed gamma camera consists of interspaced bar-shaped GAGG (Ce) crystals and tungsten absorbers. The metal bars enhance collimation for high-energy gamma photons without sacrificing the FOV. We assembled a gamma camera prototype and conducted experiments to evaluate the gamma camera’s performance for imaging 57Co, 137Cs, and 60Co point sources. (3) Results: Results show that the proposed gamma camera achieves a positioning accuracy of <3° for all gamma energies. It can clearly resolve two 137Cs point sources with 10° separation, two 57Co and two 60Co point sources with 20° separation, as well as a 2 × 3 137Cs point-source array with 20° separation. (4) Conclusions: We conclude that the proposed gamma camera design has comprehensive merits, including portability, 4π-view FOV, and good angular resolution across a wide energy range. The presented approach has promising potential in nuclear security applications.