MDRN: Multi-distillation residual network for efficient MR image super-resolution.

Super-resolution (SR) of magnetic resonance imaging (MRI) is gaining increasing attention for being able to provide detailed anatomical information. However, current SR methods often use the complex convolutional network for feature extraction, which is difficult to train and not suitable for limited computation resources in the medical scenario. To tackle these bottlenecks, we propose a multi-distillation residual network (MDRN) for more differential feature refinement, which has a superior trade-off between reconstruction accuracy and computation cost.

Comparative Efficacy of Non-contrast vs. Contrast-enhanced CT Radiomics in Predicting Coronary Artery Plaques Among Patients with Low Agatston Scores.

Rationale And Objectives: Patients with a low Agatston score often present with clinical signs and symptoms suggestive of coronary artery disease, despite having minimal calcium deposits. This study aimed to compare the efficacy of low-dose non-contrast cardiac CT with coronary computed tomography angiography (CCTA) in pericoronary adipose tissue (PCAT) radiomics for predicting coronary artery plaques, using CCTA as the reference standard.

Materials And Methods: This retrospective study analyzed 459 patients with suspected coronary artery disease and a coronary artery calcium score < 100 Agatston units, who were treated between June 2021 and December 2023 at a tertiary hospital.

Large Band Splitting in g-Wave Altermagnet CrSb.

Altermagnetism (AM), a newly discovered magnetic state, ingeniously integrates the properties of ferromagnetism and antiferromagnetism, representing a significant breakthrough in the field of magnetic materials. Despite experimental verification of some typical AM materials, such as MnTe and MnTe_{2}, the pursuit of AM materials that feature larger spin splitting and higher transition temperature is still essential. Here, our research focuses on CrSb, which possesses Néel temperature of up to 700 K and giant spin splitting near the Fermi level (E_{F}).

Impact of sports vision training on visuomotor skills and shooting performance in elite skeet shooters.

Introduction: Vision serves as a critical channel for athletes to acquire information during competitions and constitutes a vital component of their competitive ability. Through scientifically designed sports visual training, specific visual skills can be enhanced, thereby assisting athletes in achieving optimal performance in competitive settings. This study aim to explore the visuomotor abilities and shooting performance of skeet shooters through Sports Vision Training (SVT).

Absence of Altermagnetic Spin Splitting Character in Rutile Oxide RuO_{2}.

Rutile RuO_{2} has been posited as a potential d-wave altermagnetism candidate, with a predicted significant spin splitting up to 1.4 eV. Despite accumulating theoretical predictions and transport measurements, direct spectroscopic observation of spin splitting has remained elusive.

Observation of Highly Spin-Polarized Dangling Bond Surface States in Rare-Earth Pnictide Tellurides.

To generate and manipulate spin-polarized electronic states in solids are crucial for modern spintronics. The textbook routes employ quantum well states or Shockley/topological type surface states whose spin degeneracy is lifted by strong spin-orbit coupling and inversion symmetry breaking at the surface/interface. The resultant spin polarization is usually truncated because of the intertwining between multiple orbitals.

Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.

The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients' head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function.

Robust distributed fixed-time cooperative hunting control for multi-quadrotor with obstacles avoidance.

This paper addressed the robust distributed fixed-time cooperative hunting problem of multiple quadrotors subject to disturbances in obstacles environment. To handle the underactuated issue inherent in quadrotor dynamics, an inner-outer (attitude-position) loop cascade control configuration is proposed to achieve the cooperative flight control of quadrotors. For position subsystem, as the information of target cannot be accessible to all quadrotors, a distributed fixed-time observer is devised to estimate the target's information.

Coexistence of Ferromagnetism and Superconductivity at KTaO Heterointerfaces.

The coexistence of superconductivity and ferromagnetism is a long-standing issue in superconductivity due to the antagonistic nature of these two ordered states. Experimentally identifying and characterizing novel heterointerface superconductors that coexist with magnetism presents significant challenges. Here, we report the observation of two-dimensional long-range ferromagnetic order in a KTaO heterointerface superconductor, showing the coexistence of superconductivity and ferromagnetism.

L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.

In recent years, deep learning has ushered in significant development in medical image registration, and the method of non-rigid registration using deep neural networks to generate a deformation field has higher accuracy. However, unlike monomodal medical image registration, multimodal medical image registration is a more complex and challenging task. This paper proposes a new linear-to-nonlinear framework (L2NLF) for multimodal medical image registration.

Efficacy and Safety of Transarterial Chemoembolization Plus Donafenib with or without Immune Checkpoint Inhibitors as the First-Line Treatment for Unresectable Hepatocellular Carcinoma: A Propensity Score Matching Analysis.

Purpose: To compare the efficacy and safety of transarterial chemoembolization (TACE) plus donafenib with immune checkpoint inhibitors (ICIs) (T+D+I) versus TACE plus donafenib (T+D) as the first-line treatment for patients with unresectable hepatocellular carcinoma (HCC).

Methods: This retrospective study included patients with unresectable HCC who received T+D+I or T+D between June 2021 and February 2023. The tumor response was analyzed according to the modified Response Evaluation Criteria in Solid Tumors.

Exploring native and non-native English speaker teachers' perceptions of English teacher qualities and their students' responses.

Due to globalization, English has gradually become a , leading to a rising demand for proficient English teachers all over the globe. In China, more EFL teachers are being recruited, particularly at the tertiary level, with a greater preference for so-called "native English speaking teachers (NESTs)" over "non-native English-speaking teachers (NNESTs)" due to the impacts of native-speakerism. Research has shown NESTs, NNESTs, and students are often misaligned in terms of beliefs about language learning and teaching which affect teaching effectiveness as well as student achievement.

Improving performance of medical image alignment through super-resolution.

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data.

Semi-supervised lane detection for continuous traffic scenes.

Objective: This article aims to upgrade the lane detection algorithm from image to video level in order to advance automatic driving technology. The objective is to propose a cost-efficient algorithm that can handle complex traffic scenes and different driving speeds using continuous image inputs.

Methods: To achieve this objective, we introduce the Multi-ERFNet-ConvLSTM network framework, which combines Efficient Residual Factorized ConvNet (ERFNet) and Convolution Long Short Term Memory (ConvLSTM).

Synthetic CT generation from CBCT using double-chain-CycleGAN.

Purpose: Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical application in adaptive radiotherapy. To explore the potential application value of CBCT in adaptive radiotherapy, In this study, we improve the cycle-GAN's backbone network structure to generate higher quality synthetic CT (sCT) from CBCT.

A deformable patch-based transformer for 3D medical image registration.

Purpose: Medical image registration is of great importance in clinical medicine. However, medical image registration algorithms are still in the development stage due to the challenges posed by the related complex physiological structures. The objective of this study was to design a 3D medical image registration algorithm that satisfies the need for high accuracy and speed of complex physiological structures.

Quantification of epicardial fat volume using low-dose cardiac scan in patients with low calcium score.

Background: We investigated the accuracy of quantifying epicardial adipose tissue volume (EATV) using low-dose cardiac scan (EATV) and evaluated its clinical utility in predicting coronary heart disease in patients with low or mild calcification.

Methods: In total, 204 patients with clinical symptoms of coronary heart disease and coronary artery calcium score (CACS) of <100 AU were enrolled in this retrospective study. After obtaining EATV and EATV measured using computed tomography angiography (EATV), the agreement between the two measurements was evaluated using Pearson correlation coefficient and Bland-Altman analysis.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm.

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared.

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images.

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed.

Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network.

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network.

Robust 3D lane detection in complex traffic scenes using Att-Gen-LaneNet.

Robust 3D lane detection is the key to advanced autonomous driving technologies. However, complex traffic scenes such as bad weather and variable terrain are the main factors affecting the robustness of lane detection algorithms. In this paper, a generalized two-stage network called Att-Gen-LaneNet was proposed to achieve robust 3D lane detection in complex traffic scenes.