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Using a nm-level powder fabricated by a wet chemical method as precursor, the CeO2-doped WO3ceramics were prepared by the conventional solid state reaction at sintering temperatures from 600 to 1100?°C. The x-ray diffraction analysis reveals the coexistence of different WO3phases in the samples sintered at temperatures below 900?°C, whereas a single phase appears in the samples sintered above 1000?°C. No new Ce-W compound appears. As the sintering temperature increases, the electrical properties of the samples display an interesting transformation from linear to nonlinear behaviour. The measurements of scanning electron microscope, complex impedance and electrical stability indicate that a lot of grain boundary regions in the samples sintered at low temperatures strongly influences the electrical transportation. Therefore, the electrical nonlinearity is due to a basic process controlled by the back-to-back Schottky barriers at grain boundaries with suitable thickness as well as the coexistence of phases. [ABSTRACT FROM AUTHOR]

Objective. This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD). Approach. With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method. Main Results. The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance. In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems. [ABSTRACT FROM AUTHOR]

A windmill-like metasurface featuring a polarization-independent electromagnetically induced transparency (EIT) at microwave frequencies is numerically and experimentally demonstrated. The unit cell of the metasurface consists of four rotated identical metal wires, with a 45° angle between the adjacent wires. Destructive coupling between the resonance modes of the metal wires results in the emergence of a transparent window. By combining the metal wires with different degrees of symmetry, EIT effects in the metasurface show polarization-independent properties to incident linear and circular polarization waves. In addition, it is numerically demonstrated that the metasurface possesses a low-loss slow wave property with a group index of 125 and sensing capability based on the refractive index with a figure of merit of 8.73. Such a scheme may lead to many potential applications in areas of slow light and sensing. [ABSTRACT FROM AUTHOR]

We numerically demonstrate that a broadband electromagnetically induced transparency–like (EIT-like) effect can be achieved in an all-dielectric microstructure consisting of a dielectric bar and six dielectric bricks. With proper excitations, the dielectric bar and bricks serve as bright and dark elements via the Mie electric and magnetic resonances, respectively. In particular, the mutual couplings between the Mie electric and magnetic resonances induce a broad transparency window. The nature of resonances of the broadband EIT-like effect in an all-dielectric microstructure is investigated by numerical simulation and a coupled oscillator model. Results reveal that significant enhancement of coupling interactions between dielectric resonators leads to a broadband EIT-like effect. Such a dielectric EIT-like structure is promising for future applications in nonlinear optics, slow light devices, and filters. [ABSTRACT FROM AUTHOR]

Introduction. To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods. The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was trained and tested on the T1 ρ mapping data from 8 volunteers at net acceleration rates of 17, respectively. Regional T1 ρ analysis for cartilage and the brain was performed to assess the performance of RG-Net. Results. RG-Net yields a high-quality T1 ρ map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1 ρ value analysis. Conclusion. The proposed RG-Net can achieve a high acceleration rate while maintaining good reconstruction quality by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping. The generative module of our framework can also be used as an insertable module in other fast MR parametric mapping methods. [ABSTRACT FROM AUTHOR]

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method. [ABSTRACT FROM AUTHOR]

To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional important information. In this article, we propose a deep subtraction residual network based on adjacency content transfer to reconstruct noncontrast CT from contrast CT and maintain image quality comparable to that of a CT scan originally acquired without contrast. To address the slight structural dissimilarity of the paired CT images (noncontrast CT and contrast CT) due to involuntary physiological motion, we introduce a contrastive loss network derived from the adjacency content-transfer strategy. We evaluate the results of various similarity metrics (MSE, SSIM, NRMSE, PSNR, MAE) and the fitting curve (HU distribution) of the output mapping to estimate the reconstruction performance of the algorithm. To build the model, we randomly select a total of 15,405 CT paired images (noncontrast CT and contrast-enhanced CT) for training and 10,270 CT paired images for testing. The proposed algorithm preserves the robust structures from the contrast-enhanced CT scans and learns the noncontrast attenuation pattern from the noncontrast CT scans. During the evaluation, the deep subtraction residual network achieves higher MSE, MAE, NRMSE, and PSNR scores (by 30%) than those of the baseline models (BEGAN, CycleGAN, Pixel2Pixel) and better simulates the HU curve of noncontrast CT attenuation. After validation based on an analysis of the experimental results, we can report that the noncontrast CT images reconstructed by our proposed algorithm not only preserve the high-quality structures from the contrast-enhanced CT images, but also mimic the CT attenuation of the originally acquired noncontrast CT images. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) imaging can be used for early detection, diagnosis and postoperative patient monitoring of many diseases. Traditional PET imaging requires not only additional computed tomography (CT) imaging or magnetic resonance imaging (MR) to provide anatomical information but also attenuation correction (AC) map calculation based on CT images or MR images for accurate quantitative estimation. During a patient's treatment, PET/CT or PET/MR scans are inevitably repeated many times, leading to additional doses of ionizing radiation (CT scans) and additional economic and time costs (MR scans). To reduce adverse effects while obtaining high-quality PET/MR images in the course of a patient's treatment, especially in the stage of evaluating the effect of postoperative treatment, in this work, we propose a new method based on deep learning, which can directly obtain synthetic attenuation-corrected PET (sAC PET) and synthetic T1-weighted MR (sMR) images based only on non-attenuation-corrected PET (NAC PET) images. Our model, based on the Wasserstein generative adversarial network, first removes noise and artifacts from the NAC PET images to generate sAC PET images and then generates sMR images from the obtained sAC PET images. To evaluate the performance of this generative model, we evaluated it on paired PET/MR images from a total of eighty clinical patients. Based on qualitative and quantitative analysis, the generated sAC PET and sMR images showed a high degree of similarity to the real AC PET and real MR images. These results indicated that our proposed method can reduce the frequency of additional anatomical imaging scans during PET imaging and has great potential in improving doctors' clinical diagnosis efficiency, saving patients' economic expenditure and reducing the radiation risk brought by CT scanning. [ABSTRACT FROM AUTHOR]

Dynamic myocardial perfusion computed tomography (DMP-CT) is an effective medical imaging technique for coronary artery disease diagnosis and therapy guidance. However, the radiation dose received by the patient during repeated CT scans is a widespread concern of radiologists because of the increased risk of cancer. The sparse few-view CT scanning protocol can be a feasible approach to reduce the radiation dose of DMP-CT imaging; however, an advanced reconstruction algorithm is needed. In this paper, a temporal feature prior-aided separated reconstruction method (TFP-SR) for low-dose DMP-CT images reconstruction from sparse few-view sinograms is proposed. To implement the proposed method, the objective perfusion image is divided into the baseline fraction and the enhancement fraction introduced by the arrival of the contrast agent. The core of the proposed TFP-SR method is the utilization of the temporal evolution information that naturally exists in the DMP-CT image sequence to aid the enhancement image reconstruction from limited data. The temporal feature vector of an image pixel is defined by the intensities of this pixel in the pre-reconstructed enhancement sequence, and the connection between two related features is calculated via a zero-mean Gaussian function. A prior matrix is constructed based on the connections between the extracted temporal features and used in the iterative reconstruction of the enhancement images. To evaluate the proposed method, the conventional filtered back-projection algorithm, the total variation regularized PWLS (PWLS-TV) and the prior image constrained compressed sensing are compared in this paper based on studies on a digital extended cardiac-torso (XCAT) thoracic phantom and a preclinical porcine DMP-CT data set that take image misregistration into account. The experimental results demonstrate that the proposed TFP-SR method has superior performance in sparse DMP-CT images reconstruction in terms of image quality and the analyses of the time attenuation curve and hemodynamic parameters. [ABSTRACT FROM AUTHOR]

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training data without considering dose differences. However, the radiation dose difference has a great impact on the ultimate results, and lower doses increase the difficulty of restoration. Moreover, there is increasing demand to design and estimate acceptable scanning doses for patients in clinical practice, necessitating dose-aware networks embedded with adaptive dose estimation. In this paper, we consider these dose differences of input LDCT images and propose an adaptive dose-aware network. First, considering a large dose distribution range for simulation convenience, we coarsely define five dose levels in advance as lowest, lower, mild, higher and highest radiation dose levels. Instead of directly building the end-to-end mapping function between LDCT images and high-dose CT counterparts, the dose level is primarily estimated in the first stage. In the second stage, the adaptively learned low-dose level is used to guide the image restoration process as the pattern of prior information through the channel feature transform. We conduct experiments on a simulated dataset based on original high dose parts of American Association of Physicists in Medicine challenge datasets from the Mayo Clinic. Ablation studies validate the effectiveness of the dose-level estimation, and the experimental results show that our method is superior to several other DL-based methods. Specifically, our method provides obviously better performance in terms of the peak signal-to-noise ratio and visual quality reflected in subjective scores. Due to the dual-stage process, our method may suffer limitations under more parameters and coarse dose-level definitions, and thus, further improvements in clinical practical applications with different CT equipment vendors are planned in future work. [ABSTRACT FROM AUTHOR]

In this work, a small animal PET scanner named SIAT aPET was developed using dual-ended readout depth encoding detectors to simultaneously achieve high spatial resolution and high sensitivity. The scanner consists of four detector rings with 12 detector modules per ring; the ring diameter is 111 mm and the axial field of view (FOV) is 105.6 mm. The images are reconstructed using an ordered subset expectation maximization (OSEM) algorithm. The spatial resolution of the scanner was measured by using a 22Na point source at the center axial FOV with different radial offsets. The sensitivity of the scanner was measured at center axis of the scanner with different axial positions. The count rate performance of the system was evaluated by scanning mouse-sized and rat-sized phantoms. An ultra-micro hot-rods phantom and two mice injected with 18F-NaF and 18F-FDG were scanned on the scanner. An average depth of interaction (DOI) resolution of 1.96 mm, energy resolution of 19.1% and timing resolution of 1.20 ns were obtained for the detector. Average spatial resolutions of 0.82 mm and 1.16 mm were obtained up to a distance of 30 mm radially from the center of the FOV when reconstructing a point source in 1% and 10% warm backgrounds, respectively, using OSEM reconstruction with 16 subsets and 10 iterations. Sensitivities of 16.0% and 11.9% were achieved at center of the scanner for energy windows of 250–750 keV and 350–750 keV respectively. Peak noise equivalent count rates (NECRs) of 324 kcps and 144 kcps were obtained at an activity of 26.4 MBq for the mouse-sized and rat-sized phantoms. Rods of 1.0 mm diameter can be visually resolved from the image of the ultra-micro hot-rods phantom. The capability of the scanner was demonstrated by high quality in-vivo mouse images. [ABSTRACT FROM AUTHOR]

Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersampled k-space to the fully sampled images. Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied. In this paper, we propose an unsupervised deep learning method for multi-coil cine MRI via a time-interleaved sampling strategy. Specifically, a time-interleaved acquisition scheme is utilized to build a set of fully encoded reference data by directly merging the k-space data of adjacent time frames. Then these fully encoded data can be used to train a parallel network for reconstructing images of each coil separately. Finally, the images from each coil are combined via a CNN to implicitly explore the correlations between coils. The comparisons with classic k-t FOCUSS, k-t SLR, L+S and KLR methods on in vivo datasets show that our method can achieve improved reconstruction results in an extremely short amount of time. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However, PET imaging requires not only additional computed tomography (CT) imaging to provide detailed anatomical information but also attenuation correction (AC) maps calculated from CT images for precise PET quantification, which inevitably demands that patients undergo additional doses of ionizing radiation. To reduce the radiation dose and simultaneously obtain high-quality PET/CT images, in this work, we present an alternative based on deep learning that can estimate synthetic attenuation corrected PET (sAC PET) and synthetic CT (sCT) images from non-attenuation corrected PET (NAC PET) scans for whole-body PET/CT imaging. Our model consists of two stages: the first stage removes noise and artefacts in the NAC PET images to generate sAC PET images, and the second stage synthesizes CT images from the sAC PET images obtained in the first stage. Both stages employ the same deep Wasserstein generative adversarial network and identical loss functions, which encourage the proposed model to generate more realistic and satisfying output images. To evaluate the performance of the proposed algorithm, we conducted a comprehensive study on a total of 45 sets of paired PET/CT images of clinical patients. The final experimental results demonstrated that both the generated sAC PET and sCT images showed great similarity to true AC PET and true CT images based on both qualitative and quantitative analyses. These results also indicate that in the future, our proposed algorithm has tremendous potential for reducing the need for additional anatomic imaging in hybrid PET/CT systems or the need for lengthy MR sequence acquisition in hybrid PET/MRI systems. [ABSTRACT FROM AUTHOR]

The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts. [ABSTRACT FROM AUTHOR]

We propose a scheme for realizing the coherent perfect absorption (CPA) by exploiting the moderate coupling between the electric and magnetic resonators in an electromagnetically induced transparency-like (EIT-like) system. Moreover, the ideal parity-time (PT) symmetry can be established in such a passive system by precisely engineering the rate between the scattering and dissipative losses of resonators as well as their coupling. Specifically, by controlling the phase difference between two incident waves, the absorption ratio of CPA at the peak frequency can be dynamically modulated from 1 to 0. Such a scheme provides an effective route to construct absorbing devices. [ABSTRACT FROM AUTHOR]

Miniature microbial fuel cell (MFC) technology has received growing interest due to its potential applications in high-throughput screening of bacteria and mutants to elucidate mechanisms of electricity generation. This paper reports a novel miniature MFC with an improved output power density and short startup time, utilizing electrospun conducting poly(3,4-ethylenedioxythiophene) (PEDOT) nanofibers as a 3D porous anode within a 12 μl anolyte chamber. This device results in 423 μW cm−3 power density based on the volume of the anolyte chamber, using Shewanella oneidensis MR-1 as a model biocatalyst without any optimization of bacterial culture. The device also excels in a startup time of only 1hr. The high conductivity of the electrospun nanofibers makes them suitable for efficient electron transfer. The mean pore size of the conducting nanofibers is several micrometers, which is favorable for bacterial penetration and colonization of surfaces of the nanofibers. We demonstrate that S. oneidensis can fully colonize the interior region of this nanofibers-based porous anode. This work represents a new attempt to explore the use of electrospun PEDOT nanofibers as a 3D anode material for MFCs. The presented miniature MFC potentially will provide a high-sensitivity, high-throughput tool to screen suitable bacterial species and mutant strains for use in large-size MFCs. [ABSTRACT FROM AUTHOR]

Based on closed-orbit theory, the photodetachment of H- in a gradient electric field near a metal surface is studied. It is demonstrated that the gradient electric field has a significant influence on the photodetachment of negative ions near a metal surface. With the increase of the gradient of the electric field, the oscillation in the photodetachment cross section becomes strengthened. Besides, in contrast to the photodetachment of H- near a metal surface in a uniform electric field, the oscillating amplitude and the oscillating region in the cross section of a gradient electric field also become enlarged. Therefore, we can use the gradient electric field to control the photodetachment of negative ions near a metal surface. We hope that our results will be useful for understanding the photodetachment of negative ions in the vicinity of surfaces, cavities, and ion traps. [ABSTRACT FROM AUTHOR]

A new varistor system based on terbium oxide is introduced in this paper. The ceramic samples were prepared using the standard ceramic method and sintered in air at 1300 °C for 2 h. From the I-V measurements, the samples exhibit varistor action with the nonlinear coefficient a = 3.9 and the breakdown voltage Eb = 40 V mm[?]1 and possess excellent electrical stability up to 70 °C. X-ray diffraction reveals that the ceramic is a single phase structure. It is proposed that the varistor action can be attributed to the adsorbed oxygen on the grain surfaces. This is on the basis of the barrier formation model for metal oxide varistor systems and the intrinsic characteristics of terbium oxide ceramic. [ABSTRACT FROM AUTHOR]

The hazard of Hg ion pollution triggers the motivation to explore a fast, sensitive, and reliable detection method. Here, we design and fabricate novel 36-nm-thick Ag–Au composite layers alternately deposited on three-dimensional (3D) periodic SiO2 nanogrids as surface-enhanced Raman scattering (SERS) probes. The SERS effects of the probes depend mainly on the positions and intensities of their localized surface plasmon resonance (LSPR) peaks, which is confirmed by the absorption spectra from finite-difference time-domain (FDTD) calculations. By optimizing the structure and material to maximize the intrinsic electric field enhancement based on the design method of 3D periodic SERS probes proposed, high performance of the Ag–Au/SiO2 nanogrid probes is achieved with the stability further enhanced by annealing. The optimized probes show the outstanding stability with only 4.0% SERS intensity change during 10-day storage, the excellent detection uniformity of 5.78% (RSD), the detection limit of 5.0×10−12 M (1 ppt), and superior selectivity for Hg ions. The present study renders it possible to realize the rapid and reliable detection of trace heavy metal ions by developing high-performance 3D periodic structure SERS probes by designing novel 3D structure and optimizing plasmonic material. [ABSTRACT FROM AUTHOR]

We propose a design method for a passive polarization agile antenna based on the electromagnetically induced transparency-like (EIT-like) effect. Benefiting from strong dispersion properties governed by EIT-like effects, the proposed structure can endow electromagnetic waves transmitted through it with quite different polarization states at very close frequencies. The experimental measurement was conducted to demonstrate agile polarization controls by placing a designed EIT-like waveplate in front of a standard microwave horn antenna. Results show that the polarization state of radiated waves by the horn antenna with a waveplate can be easily transformed among linear, circular and elliptical polarizations through fine-tuning the operating frequency, which is extremely important for certain special applications, e.g. electronic countermeasures. Our scheme could also be utilized at higher operating frequencies by the simply scaling principle. [ABSTRACT FROM AUTHOR]

ZnO nanofibers are synthesized by an electrospinning method and characterized by x-ray diffraction (XRD) and scanning electron microscopy (SEM). Two types of gas sensors are fabricated by loading these nanofibers as the sensing materials and their performances are investigated in detail. Compared with the sensors based on traditional ceramic tubes with Au electrodes (traditional sensors), the sensors fabricated by spinning ZnO nanofibers on ceramic planes with Ag-Pd electrodes (plane sensors) exhibit much higher sensing properties. The sensitivity for the plane sensors is about 30 to 100 ppm ethanol at 300degC, while the value is only 13 for the traditional sensors. The response and recovery times are about 2 and 3s for the plane sensors and are 3 and 6s for the traditional sensors, respectively. Lower minimum-detection-limit is also found for the plane sensors. These improvements are explained by considering the morphological damage in the fabricating process for traditional sensors. The results suggest that the plane sensors are more suitable to sensing investigation for higher veracity. [ABSTRACT FROM AUTHOR]