Different Fields Of View Research Articles

Design of a compact off-axis freeform three-mirror system in a circular configuration.

A compact off-axis three-mirror system, especially with a wide field of view and small f-number, continues to present challenges in the optical design field. To design a compact off-axis three-mirror system based on a freeform surface, an optimization method with a circular configuration and four parameters is proposed. In the proposed method, the f-number and angles of mirrors are combined to optimize and achieve high-quality imaging, which means the modulation transfer function is close to the diffraction limit. To evaluate the performance of the proposed method, four design examples with different fields of view (4∘×4∘, 6∘×6∘) and f-numbers (2, 1.75) were created. The results showed that the compact off-axis three-mirror system based on a freeform surface can be designed with a wide field of view, small f-number, and high-quality imaging using the proposed method. Unlike other design methods, the surface parameters of mirrors are obtained with only one calculation and are close to the final optimization results, which saves both time and resources for optimization. The findings indicate that the method is accurate and effective for designing compact off-axis three-mirror systems with freeform surfaces.

Evaluation of the dosimetry and centralization of scout-view function in CBCT.

This study evaluated the centralization of the region of interest (ROI) in acquisition of the CBCT images, when the freely positionable scout-view (SV) function is applied. Additionally, the dosimetry of the acquired images was assessed in the SV function alone as well as in complete tomographic image in two different fields of view (FOV) (50x50 and 78x150mm). A three-location device was created to accommodate the dosimeters and the specimens, in the right, middle and left location during image acquisition. For dose assessment, thermoluminescent dosimeters were irradiated within the FOV and analyzed in a portable reader. For ROI evaluation, three specimens of gutta-percha stick were placed on the same device and the CT scans were acquired (CBCT OP 300 Maxio device, 90kV, 13mA, 85 µm voxel size, FOV of 50X50mm), with and without the SV, in three positions (3-9, 1-7 and 5-11 o'clock), simulating different regions of the mouth. Two image evaluations were performed, an objective and subjective. There was a slight percentage increase (1.36% to 1.40%) of the radiation dose with the use of SV. The distances were significantly greater in the images acquired without SV (p < 0.05). Every image obtained with SV was classified as being at the FOV's center. In conclusion, the results demonstrated that SVs function is effective to centralize the ROI in the FOV, increasing the scan precision and avoiding repetitions due to positioning errors.

Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.

Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population, scanning devices, imaging protocols, and so on, significant degradation in the performance of model inference results is prone to occur when models trained with domain-specific (usually institution-specific) datasets are directly applied to new (other centers/institutions) domain data. In this paper, we propose a novel unsupervised domain adaptation method based on adversarial learning to address pancreas segmentation challenges with the lack of annotations and domain shift interference. A 3D semantic segmentation model with attention module and residual module is designed as the backbone pancreas segmentation model. In both segmentation model and domain adaptation discriminator network, a multiscale progressively weighted structure is introduced to acquire different field of views. Features of labeled data and unlabeled data are fed in pairs into the proposed multiscale discriminator to learn domain-specific characteristics. Then the unlabeled data features with pseudodomain label are fed to the discriminator to acquire domain-ambiguous information. With this adversarial learning strategy, the performance of the segmentation network is enhanced to segment unseen unlabeleddata. Experiments were conducted on two public annotated datasets as source datasets, respectively, and one private dataset as target dataset, where annotations were not used for the training process but only for evaluation. The 3D segmentation model achieves comparative performance with state-of-the-art pancreas segmentation methods on source domain. After implementing our domain adaptation architecture, the average dice similarity coefficient (DSC) of the segmentation model trained on the NIH-TCIA source dataset increases from 58.79% to 72.73% on the local hospital dataset, while the performance of the target domain segmentation model transferred from the medical segmentation decathlon (MSD) source dataset rises from 62.34% to 71.17%. Correlations of features across data domains are utilized to train the pancreas segmentation model on unlabeled data domain, improving the generalization of the model. Our results demonstrate that the proposed method enables the segmentation model to make meaningful segmentation for unseen data of the training set. In the future, the proposed method has the potential to apply segmentation model trained on public dataset to clinical unannotated CT images from local hospital, effectively assisting radiologists in clinicalpractice.

High-precision Multichannel Solar Image Registration Using Image Intensity

Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.

Uniformity improvement of two-dimensional surface relief grating waveguide display using particle swarm optimization.

Augmented reality head-mounted displays (AR-HMDs) based on diffractive waveguides have been a challenging and rewarding research topic focusing on near-eye displays. The size of the exit pupil and uniformity of the image illuminance are two important factors that affect the display performance of the diffractive waveguide. In this paper, a novel method for optimizing high uniformity of two-dimensional (2D) diffractive waveguide is proposed. A straight-line 2D surface relief grating (SRG) waveguide with divided grating regions is designed. An illuminance uniformity evaluation model of the energy propagation process is established, and non-sequential ray tracing is utilized to optimize the diffraction efficiency of multi-regions grating to achieve illuminance uniformity distribution. Then, the uniformity distribution of the diffraction efficiency in different fields of view (FOVs) is realized by combining the particle swarm optimization (PSO) algorithm and rigorous couple wave analysis (RCWA) to optimize the grating structural parameters, which further ensures the uniformity of the exit pupil illuminance and angular illuminance. The waveguide with exit pupil expansion (EPE) has exit pupil size of 16 mm × 14 mm at an eye relief (ERF) of 20 mm, exit pupil illuminance uniformity of 91%, and angular uniformity illuminance of 64%.

Femtosecond laser 3D printed micro objective lens for ultrathin fiber endoscope

The most important optical component in an optical fiber endoscope is its objective lens. To achieve a high imaging performance level, the development of an ultra-compact objective lens is thus the key to an ultra-thin optical fiber endoscope. In this work, we use femtosecond laser 3D printing to develop a series of micro objective lenses with different optical designs. The imaging resolution and field-of-view performances of these printed micro objective lenses are investigated via both simulations and experiments. For the first time, multiple micro objective lenses with different fields of view are printed on the end face of a single imaging optical fiber, thus realizing the perfect integration of an optical fiber and objective lenses. This work demonstrates the considerable potential of femtosecond laser 3D printing in the fabrication of micro-optical systems and provides a reliable solution for the development of an ultrathin fiber endoscope.

Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism.

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task.

Incidental findings on coronary computed tomography in women with selected reproductive disorders

ObjectiveTo determine the prevalence of incidental findings (IFs) on coronary computed tomography (CCT) in women aged 45–55 years and previously diagnosed with reproductive disorders such as polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI) or preeclampsia (PE).MethodsA total of 486 middle-aged women with PCOS (n = 101), POI (n = 97) or a history of PE (n = 288) underwent a CCT as part of a prior prospective study. IFs were categorized by their significance (minor, moderate and major). Follow-up information was collected from patients’ records. To investigate the impact of different field of views (FOVs), a subset of scans was analyzed in full FOV and small FOV.ResultsIn 96/486 (19.8%) women, one or more IFs were detected, of which 54/486 (11.1%) were classified as moderate/major and 48/486 (9.9%) required follow-up. A moderate/major IF was detected in 16/101 (15.9%) women with PCOS, 13/97 (13.4%) women with POI and 25/288 (8.7%) women with a history of PE. In 78 women with an IF detected in the full FOV, the IF was still visible in 60 (76.9%) women in the small FOV. In the full FOV, 46 women required follow-up, but using the small FOV this was reduced to 30 women.ConclusionUsing CCT as a cardiovascular disease screening tool in women with selected reproductive disorders increases the probability of detecting IFs that can cause anxiety and may generate extra costs, but can also reveal clinically relevant findings. Using a small FOV centered around the heart resulted in a lower prevalence of IFs and required less follow-up.

Comparison of the accuracy of linear measurements in CBCT images with different field of views

This study sought to investigate the effect of the field of view (FOV) on linear measurements of cone beam computed tomography (CBCT) images. Methods: In this in vitro study, five dry human skulls were used. After using red wax to simulate soft tissue, the skulls were scanned using Galileos CBCT scanner (Sirona, Bensheim, Germany) with exposure parameters of 85 kVp and 21 mAs and voxel size of 0.280 mm; once with FOV of 15 cm × 8 cm and once again with 15 cm × 15 cm. The measured distances were the distance between the center of the bilateral mental foramen in the axial view (MM), the distance between the alveolar crest and the mandibular inferior border in the sagittal view on the midline (CB), and the depth of the socket of the left mandibular central incisor (L1). Descriptive statistics as well as Pearson’s correlation coefficient were used for statistical analysis (α = 0.05) using SPSS software (v. 25, IBM, NY, USA). Results: The measurements obtained with small and large FOV and with the dry skull were not significantly different (p &gt; 0.05). The measurements obtained in small FOV had excellent correlation coefficient when compared with those obtained with the dry skull, with values of 0.890 for MM, 0.954 for CB, and 0.921 for L1 (p &lt; 0.001). The measurements in large FOV also had excellent correlation coefficient when compared with those on the dry skull, with values of 0.894 for MM, 0.949 for CB, and 0.902 for L1 (p &lt; 0.001). Conclusion: According to our findings, linear measurements obtained by CBCT scans in small and large FOVs were not significantly different than those on dry skulls. Since the linear measurements are accurate regardless of FOV size, selection of FOV must be based on patient factors, such as area of interest and radiation dose.

Analysis of Swirl Number Effects on Effusion Flow Behavior Using Time-Resolved Particle Image Velocimetry

Abstract The analysis of the interaction between the swirling and cooling flows, promoted by the liner film cooling system, is a fundamental task for the design of turbine combustion chambers since it influences different aspects such as emissions and cooling capability. In particular, high turbulence values, flow instabilities, and tangential velocity components induced by the swirling flow deeply affect the behavior of effusion cooling jets, demanding for dedicated time-resolved near-wall experimental analysis. The experimental setup of this work consists of a non-reactive single sector linear combustor test rig scaled up with respect to engine dimensions; the test section was equipped with an effusion plate with standard inclined cylindrical holes to simulate the liner cooling system. The rig was instrumented with a 2D time-resolved particle image velocimetry system, focused on different field of views. The degree of swirl for a swirling flow is usually characterized by the swirl number, Sn, defined as the ratio of the tangential momentum flux to axial momentum flux. To assess the impact of such parameter on the near-wall effusion behavior, a set of three different axial swirlers with swirl number equal to Sn = 0.6–0.8–1.0 were designed and tested in the experimental apparatus. An analysis of the main flow field by varying the Sn was first performed in terms of average velocity, root mean square, and Tu values, providing kinetic energy spectra and turbulence length scale information. In a second step, the analysis was focused on the near-wall regions: the strong effects of Sn on the coolant jets were quantified in terms of vorticity analysis and jet oscillation.

Quantitative Analysis of Cone-Beam Computed Tomography Artifacts Induced by Nonmetallic Root Canal Filling Materials Using Different Fields of View: In Vitro Study.

Cone-beam computed tomography (CBCT) imaging artifacts can hinder accurate diagnosis of several conditions. The aim of this study was to quantify CBCT artifacts created by nonmetallic root canal filling materials using two fields of view (FOV). Root canals of twenty extracted maxillary central incisors (n = 20) were instrumented and randomly divided into four equal groups: canals in Group 1 were filled with gutta-percha, canals in Group 2 with mineral trioxide aggregate (MTA) and gutta-percha, canals in Group 3 with gutta-percha and glass-fiber posts, and canals in Group 4 with MTA and glass-fiber posts. Each tooth was mounted on a silicon block and scanned three times using a CBCT machine, first with a prefilling scan (control) and then with postfilling scans, using two different FOV (40∗50 mm and 80∗50 mm). Imaging software was used to measure grayscale pixel values of canal cross sections. Data were analyzed using t-test and paired t-test statistical tests, with statistical significance set at p ≤ 0.05. Dentin at the apical and middle third of the teeth showed a significant decrease in minimum grayscale values in association with gutta-percha (p = 0.027, p = 0.034). However, a wide 80∗50 FOV showed a significant increase in maximum grayscale values of coronal (p = 0.048) and apical dentin (p = 0.049). Glass-fiber posts in middle third cross sections also corresponded to an increase in maximum grayscale values (p = 0.018). Gutta-percha produced dark band artifacts in the root's middle and apical thirds, whereas white streak artifacts were produced by gutta-percha in the coronal third and glass-fiber posts in the coronal and middle thirds when imaged with a wider FOV. A smaller FOV is therefore recommended for CBCT imaging, as it produces fewer artifacts.

Sagnac Interferometer Based Digital Shearography for Simultaneous Dual Sensitivity Measurement

This paper presents a single camera digital shearography system with dual sensitivity to enable simultaneous measurement of minor and large shearograms in a single field measurement. Digital shearography has many benefits, such as its non-contact measurement, high sensitivity and resistance to turbulence. It has been successfully used for testing of minor deformation gradients in nondestructive testing (NDT). Most digital shearography systems can only work in a single measurement field. As a result, minor shearograms may be overlooked, resulting in incomplete detection in a full-field measurement. The application must allow for multiple measurements in different fields of view or overloading to reveal small defects. The former increases costs, while the latter can result in huge surface deformation that may mask important information. A novel dual sensitivity shearographic system that can simultaneously identify minor and large shearograms in a single field measurement has been developed. The setup is based on the modified Sagnac Interferometer, which uses a polarized Michelson interferometer as a substitute for one of its three mirrors to create additional shearing channels. Two pairs of orthogonally polarized waves carry the large and small field of view information individually without cross-interference on the target screen. The carrier frequency spatial phase shift method is adopted to separate the signal on the frequency domain. The system's performance for dynamic imaging of an object with defects was experimentally investigated. Theory derivation and description of the setup are shown in detail in this paper.

Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes

Data from manual healthspan assays of the nematode Caenorhabditis elegans (C. elegans) can be complex to quantify. The first attempts to quantify motor performance were done manually, using the so-called thrashing or body bends assay. Some laboratories have automated these approaches using methods that help substantially to quantify these characteristic movements in small well plates. Even so, it is sometimes difficult to find differences in motor behaviour between strains, and/or between treated vs untreated worms. For this reason, we present here a new automated method that increases the resolution flexibility, in order to capture more movement details in large standard Petri dishes, in such way that those movements are less restricted. This method is based on a Cartesian robot, which enables high-resolution images capture in standard Petri dishes. Several cameras mounted strategically on the robot and working with different fields of view, capture the required C. elegans visual information. We have performed a locomotion-based healthspan experiment with several mutant strains, and we have been able to detect statistically significant differences between two strains that show very similar movement patterns.

Simultaneous multiple-level magnification selective plane illumination microscopy (sMx-SPIM) imaging system

We report an optical-based microscopy imaging technology—simultaneous multiple-level magnification selective plane illumination microscopy (sMx-SPIM) imaging system—that addresses a longstanding technological challenge of obtaining images, specifically of biological specimen non-destructively, at different field of views (FOVs) and spatial resolutions (or magnification powers) simultaneously in real-time. This imaging system provides not only 3D images but also time-resolved sequential images with temporal resolution ∼ msec. Magnification powers (or FOVs) of the individual images can be controlled independently that is achieved by housing two separate detection arms, in SPIM imaging system, fitted with objective lenses of different magnification powers. This unique feature holds promises to observe and study: (a) sub-microscopic details and entire structure of biological specimen side-by-side simultaneously and (b) spatio-temporal dynamics of functional activities of biological specimen. For validation study of robustness of the proposed sMx-SPIM imaging system, experiments are conducted in various biological samples including Danio rerio (zebrafish) embryo, Drosophila melanogaster, Allium cepa root, and A549 cell line. Experimental results demonstrate that the study is of significant impacts from two aspects, viz., technological implication and biological applications.

A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security

The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.

Frequency-Scanned Monopulse Antenna for RSSI-Based Direction Finding of UHF RFID Tags

A compact printed-circuit leaky-wave antenna is designed to synthesize frequency-scanned monopulse patterns in the UHF RFID band from 902 to 928 MHz. A high-permittivity ceramic substrate is used to maximize the angular scanning range in such a narrow band. Two operating modes, i.e., narrow-field and wide-field modes, can be chosen by properly selecting the frequency channels and antenna ports. This allows to adapt to different fields of view while maximizing the maximum reading distance, as demonstrated with a fabricated prototype.

Design and experiment of online monitoring system for long-term culture of embryo

In the study of embryo development process, the morphological features at different stages are essential to evaluate developmental competence of the embryo, which can be used to optimize and improve the system for in-vitro embryo culture. In this paper, an online monitoring system was designed for long-term culture of embryos, based on a monitoring strategy of low-magnification search and high-magnification observation. Three optical modules of 4× phase contrast, 10× and 20× Hoffman modulation phase contrast were configured in this system to meet the requirements of different fields of view, especially when the size of the embryo increases during the culture. Using an optomechanical system matching design, an error control and alignment test, the resolution of optical imaging was guaranteed, and a relief stereoscopic imaging with high contrast of embryos was obtained. Through low-magnification field of view to identify and locate embryos and high-magnification field of view to capture the details, the system realized online tracking and monitoring of embryos. In addition, we developed and verified an embryo identifying and locating algorithm based on image contour area and definition evaluation. The online monitoring system of in-vitro embryo culture proposed in this paper can track and record the morphological features of embryos without affecting the embryo development, providing a basis for the assessment of embryo development and the optimization of in-vitro culture system.

Preliminary results of water cloud and aerosol properties in the Yinchuan area using a Multi-wavelength lidar based on dual field of view

Aerosol-cloud interaction has an extremely important impact on regional and even global climate and environmental change. It is undoubtedly of great scientific significance and research value to study its impact and mechanism on atmospheric radiation balance and climate change. As a active remote sensing, lidar can be employed to study aerosol-cloud interaction. In this paper, A novel multi-wavelength lidar system equipped with a dual field of view device is developed to measure the optical and microphysical parameters of water clouds. The scattered light is detected by the inner and outer field of view, respectively. For verifying the feasibility of the novel lidar system, some preliminary experiments were carried out at North Minzu University (106°06′E, 38°29′N) in the Yinchuan, China. The experiments shows that the novel lidar system can effective detect the optical and microphysical property parameters of water clouds, including extinction coefficient, effective particle radius and liquid water content, with a cloud base height of less than 5 km. Compared with traditional lidar systems that detect optical properties alone, it can use return signals from different fields of view to detect the microphysical properties of warm clouds and thin layered clouds, therefore, it is of great practical significance to develop the cloud water resources efficiently and improve the accuracy of artificial rainfall operations.

LF-UNet - A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images.

Computer-assistant diagnosis of retinal disease relies heavily on the accurate detection of retinal boundaries and other pathological features such as fluid accumulation. Optical coherence tomography (OCT) is a non-invasive ophthalmological imaging technique that has become a standard modality in the field due to its ability to detect cross-sectional retinal pathologies at the micrometer level. In this work, we presented a novel framework to achieve simultaneous retinal layers and fluid segmentation. A dual-branch deep neural network, termed LF-UNet, was proposed which combines the expansion path of the U-Net and original fully convolutional network, with a dilated network. In addition, we introduced a cascaded network framework to include the anatomical awareness embedded in the volumetric image. Cross validation experiments showed that the proposed LF-UNet has superior performance compared to the state-of-the-art methods, and that incorporating the relative positional map structural prior information could further improve the performance regardless of the network. The generalizability of the proposed network was demonstrated on an independent dataset acquired from the same types of device with different field of view, or images acquired from different device.

Research on Fundus Image Mosaic Method Based on Genetic Algorithm

Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be spliced is a feature, and the genetic algorithm is used to solve the optimal parameters to achieve the maximum similarity. By smoothing the image, a clear image with minimum noise is obtained. Experimental results show that the proposed algorithm can effectively realize the image mosaic of the fundus. The method proposed in this paper can provide support for high-precision automatic stitching of multiple single-mode color fundus images.