Optical-driven miniature robots: driving mechanism, applications and future trends.

Miniature robots can complete complex tasks at the micro-scale, which have shown great application potential in fields such as biomedicine and environmental monitoring. As a renewable energy source, light is widely used in energy and information transmission. With the maturity of beam modulation and optical microscope technology, optical-driven miniature robots have become a hot topic in the field of miniature robotics due to their programmable nature, high resolution, non-contact nature, high precision, and good biocompatibility.

Diff5T: Benchmarking human brain diffusion MRI with an extensive 5.0 Tesla k-space and spatial dataset.

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.

A Near-Field Coupling Array Enables Parallel Imaging and SNR Gain in MRI.

Wireless radiofrequency (RF) coils based on metasurfaces hold great promise for improving clinical magnetic resonance imaging (MRI) workflows by eliminating the need for cable connections to the patient bed and simplifying coil structures. A fundamental requirement for their clinical adoption is the ability to support parallel imaging, which reduces examination time and improves efficiency. Moreover, the implementation of parallel imaging often comes at the expense of image signal-to-noise ratio (SNR).

Rapid 3D Saturation Transfer Imaging for Simultaneous Phosphocreatine and Glycogen Mapping in Human Muscle.

Objective: Phosphocreatine (PCr) and glycogen are key metabolites underpinning the skeletal muscle contractions. Simultaneous 3D imaging of these metabolites is valuable for understanding heterogeneous energetic events. While saturation transfer (ST) MRI can detect metabolites, 3D ST acquisition generally requires long scan times.

Multi-Objective Optimization of Injection Molding Process Parameters of Car Lamp Shell Based on Grey Correlation Analysis.

In order to improve the injection molding quality of the car lamp shell, orthogonal test, signal-to-noise ratio, gray correlation analysis, and CRITIC weight method were used to analyze the influence of mold temperature, melt temperature, injection time, velocity to pressure control, pressure holding pressure and pressure holding time on the shrinkage index and the total deformation of warpage, and fully consider the difference and correlation between the evaluation parameters. The multi-objective optimization is transformed into single-objective optimization, and the optimal parameter set is obtained. The experimental results show that, compared with the initial analysis results, the indentation index of the headlight shell is reduced by 33.

Wearable Medical Devices: Application Status and Prospects.

Electronic skin (E-skin) refers to a portable medical or health electronic device that can be worn directly on the human body and can carry out perception, recording, analysis, regulation, intervention and even treatment of diseases or maintenance of health status through software support. Its main features include wearability, real-time monitoring, convenience, etc. E-skin is convenient for users to wear for a long time and continuously monitors the user's physiological health data (such as heart rate, blood pressure, blood glucose, etc.

[Interpretation of Clinical Practice Specifications for Permanent Tooth Extraction (2023 Edition)].

In May 2023, the Chinese Stomatological Association promulgated the group standard of "Clinical Practice Specifications for Permanent Tooth Extraction". These specifications were formulated after repeated discussions and revisions guided by relevant literature and the opinions of well-known experts in the field across the country. However, the content of the group standard is not elaborated and is limited to its writing form and requirements.

Progressive Semantic Enhancement Network for Hyperspectral and LiDAR Classification.

The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data is gaining attention for its improved classification accuracy. However, effectively integrating the rich spectral information of HSI and the elevation features of LiDAR has remained a challenge in multimodal fusion. This article proposes a novel approach called progressive semantic enhancement network (PSENet) for hyperspectral and LiDAR classification based on a progressive joint spatial-spectral attention mechanism.

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI.

Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data.

Novel method for identifying the heaviest QED atom.

QED atoms are composed of unstructured and point-like lepton pairs bound together by the electromagnetic force. The smallest and heaviest QED atom is formed by a ττ pair. Currently, the only known atoms of this type are the ee and μe atoms, which were discovered 64 years ago and remain the sole examples found thus far.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks.

DPP: deep phase prior for parallel imaging with wave encoding.

In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.

Background: The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module.

K-UNN: k-space interpolation with untrained neural network.

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

Background: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data.

Purpose: To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free.

SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging.

Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly.

Identification and characterization of two new species causing rust on in China.

A serious rust disease was found in Gansu Province, China. The disease incidence is approximately 80-90%. We also found rust disease on both and in the same location.

Graph-in-Graph Convolutional Network for Hyperspectral Image Classification.

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation.

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images.

Purpose: To develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI).

Methods: MRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall.

Research on sunken & submerged oil detection and its behavior process under the action of breaking waves based on YOLO v4 algorithm.

Marine oil spill pollution is one of the most serious marine pollution issues. Aiming at the problems of low accuracy and slow speed in the process of detecting the behavior of sunken and submerged oil by traditional methods, a technology of sunken & submerged oil tracking based on YOLO v4 (YOLO refers to 'you look only once') algorithm is proposed in this paper. The image data used in this study are pictures of real oil pollution moving under breaking waves, and they are collected in the laboratory.

Fast multi-parametric imaging in abdomen by corrected dual-flip angle sequence with interleaved echo acquisition.

Purpose: To achieve simultaneous T /proton density fat fraction (PDFF)/ mapping in abdomen within a single breadth-hold, and validate the accuracy using state-of-art measurement.

Theory And Methods: An optimized multiple echo gradient echo (GRE) sequence with dual flip-angle acquisition was used to realize simultaneous water T (T )/PDFF/ quantification. A new method, referred to as "solving the fat-water ambiguity based on their T difference" (SORT), was proposed to address the fat-water separation problem.