Highly homogeneous magnetic fields in magnetic resonance spectrometers are an important guarantee for improving the quality of spectra for chemical structure analysis and kinetic information. The active shimming process using a shimming power supply to drive a shimming coil to produce a compensating electromagnetic field is an essential way to improve the homogeneity of the magnetic field. The high accuracy and stability of the shimming power supply’s output are crucial indicators for improving the active shimming capability and maintaining the shimming results. The paper develops a high-precision, high-stability, low-ripple active shimming power supply system for magnetic resonance spectrometers, and designs a shimming current driver (constant current source) with output state monitoring based on negative feedback control, coupled with an MCU control platform and host computer system to complete the closed-loop transmission of control commands and data between hardware and software, to achieve digital constant current output control. Under R=2.84 Ω, L=25.5 µH shimming coil load, the full-scale output response time is less than 18.9 µs, the output ripple peak-to-peak control is 30 mV, the positive and negative current output symmetry is good, and the maximum output deviation is 4.8‰ for long time operation. This power supply was used in a nuclear magnetic resonance spectrometer system using 0.5 T-Halbach configuration magnets, and the magnetic field homogeneity was optimised from 24.48 ppm (10^-6) to 2.72 ppm by driving a first-order shimming coil. This work contributes to the integration of compact nuclear magnetic resonance spectrometer systems and the applications related to active shimming.

In recent years, Halbach magnet has been extensively used in miniaturized NMR spectrometers. However, the inhomogeneity of the magnetic field of permanent magnets poses a challenge to passive shimming method. In this paper, we conducted a passive shimming study of Halbach permanent magnet array structure which is mechanically adjustable. We modeled the relationship between the radial position of the magnetic blocks and magnetic field homogeneity. Then, an optimization algorithm combining the Levenberg-Marquardt method and quasi-Newton method was utilized to optimize the magnetic field homogeneity by adjusting the radial positions of the magnetic blocks. With this approach, the homogeneity of a 1.03 T Halbach magnet was improved from 7 391×10^-6 to 154.23×10^-6 in a sphere with a radius of 2.5 mm. This work provides a flexible and convenient passive shimming method for compact Halbach magnet, which has the potential to be applied in NMR spectrometers and other instruments that require high magnetic field homogeneity.

Brain age has become an important analysis object in the diagnosis and mechanism research of neurodegenerative diseases. There is no consistent conclusion on whether major depression increases the brain age of patients, and few studies in this direction have been conducted in the Chinese population. In this paper, a REST-meta-MDD (resting-state functional magnetic resonance imaging dataset of major depressive disorder) dataset collected from 25 hospitals in China was used to construct a convolutional neural network model based on high-resolution T1-weighted three-dimensional magnetic resonance images of brain structures to predict the brain age of patients and calculate the difference from the actual age. The mean absolute error and correlation coefficients of the final results were 3.16 and 0.93, and the mean brain age of the patients with major depression increased by 3.94 years compared with the healthy group, further confirming that major depression accelerates brain aging, and the severity of the disease is related to the gender, age, and education of the patients. Compared with the traditional machine learning algorithms, the average absolute error of the results obtained by this model is smaller and the correlation coefficient is higher.

The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.

Nuclear magnetic resonance (NMR) technology can directly detect hydrogen signals in hydrogen-containing fluids, such as water and hydrocarbon. It can provide quantitative characterization of the fluid content and surrounding environment, thus finding extensive application in oil and gas exploration field. However, when measured samples containing ultra-fast relaxation components, such as shale oil and gas, the reliability of measurement results tends to be very low because only a small number of front-end signals in Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence can be obtained for the fast relaxation components. To address this problem, a multiple micro echo interval measurement (MCTEM) method was proposed in this research. In the measurement stage, the proposed method acquired more NMR front-end signals. In the inversion stage, multiple echo data were adopted for the joint inversion. It contributed to forming an accurate fast relaxation T₂ spectrum. MCTEM method was validated through numerical simulations and rock physics experiments. It was concluded from the results that MCTEM method could improve the problem of peak disappearance for fast relaxation components in T₂ spectrum caused by long echo interval. MCTEM method could enhance the measurement accuracy of fast relaxation signals effectively.

Investigating the moisture migration during the wood drying process can help improve wood utilization. Single-sided nuclear magnetic resonance (NMR) technology facilitates such investigation with its advantage in conducting one-dimensional measurements along different directions of wood, allowing for the detection of moisture transfer at different depths along the axial and transverse directions during the wood drying process. This research focused on Pinus sylvestris var.mongolica wood, on which a glue sealing technique was employed to ensure that the moisture only transfers along the axial or transverse axis, and the apparent transverse relaxation time (T_2app) was utilized to delve into the changes of moisture content at various depths during the drying process. The results showed that at the first 2 hours of the drying process, there was a little free water near the evaporation surface of Pinus sylvestris var.mongolica wood, followed by a scarcity of free water during the drying process, and a noticeable moisture content gradient was observed near the evaporation surface. When the moisture transferred along the axial direction, the farther the moisture was away from the evaporation surface, the more uniform the moisture distribution was. When the moisture transferred along the tangential direction, the farther the moisture was away from the evaporation surface, the more obvious the moisture difference in each layer was. By single-sided NMR technology, it is possible to ascertain the moisture content of wood at various depths, thereby offering a theoretical framework for revealing the migration mechanism of water within wood.

With their high reliability, small size and low power consumption, rubidium atomic clocks are widely used in the fields of navigation, communication, etc. In particular, rubidium atomic clocks for navigation satellites have developed excellent stability. However, their inherent frequency drift characteristics (about E-12 to E-13/day) will deteriorate their long-term performance and affect the autonomous punctual timing of satellites. In this paper, we fully analyzed the frequency data of high-performance rubidium atomic clocks, aiming to figure out the physical mechanism behind the frequency drift. A high-precision frequency drift compensation scheme is proposed and experimentally verified. The results show that the drift rate of high-performance rubidium clock can be maintained in the order of E-15/day within 60 days without external taming, and the day stability can reach the order of E-15 (Allan variance), which greatly improves the autonomous punctual timing ability of rubidium atomic clock．

Laplace NMR can provide information on diffusion coefficients or relaxation time, serving as a powerful technology for studying molecular structure, dynamics, and interactions in samples. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms associated with the inverse Laplace transform. In this paper, we first discuss the ill-posed nature of the spectrum reconstruction problem for Laplace NMR, then revisit the classic regularization-based reconstruction algorithms and introduce the state-of-the-art deep-learning-based methods. In conclusion, the advantages and disadvantages of these algorithms are summarized, and future improvements for Laplace NMR signal processing methods are prospected.

Magnetic resonance elastography (MRE) is a method to estimate biomechanical properties of soft tissues by recording shear wave propagation using MR imaging. The wave excitation is produced by an external actuator and the properties are inversely calculated based on the wave equation. Biomechanical properties of brain tissue, especially the viscoelastic properties, are closely related to the growth, aging, and disease of brain. This review first introduces the theoretical background of MRE, followed by the physical meaning of the viscoelastic parameters and wave equations used for inversion. Scanning protocols for MRE, along with a specific example focusing on brain MRE, are also described. The paper presents various clinical applications of brain MRE, with a specific emphasis on brain tumors and neurodegenerative diseases. The application of viscoelastic properties as biomarkers in fundamental scientific research, disease diagnosis, and prognosis is discussed. We further highlight the current trends in brain MRE research covering both technical and clinical aspects, providing a reference for future neuroscience research and clinical applications.

As the aging population increases, the prevalence of cardiovascular disease rises annually. In this context, the evaluation of cardiac function using medical imaging techniques plays a pivotal role in the diagnosis and treatment of cardiovascular disease. Cardiac segmentation is a prerequisite for assessing cardiac function and has been closely studied by clinicians and scientific researchers. This paper provides a comprehensive review of the literature from the past decade on cardiac segmentation, categorizing the studies into traditional segmentation approaches and deep learning methodologies. Emphasis is placed on the detailed discussion of segmentation methods based on active contours and atlas models; deep learning algorithms based on U-Net and full convolution neural network (FCN) are also extensively discussed. In particular, this paper elaborates various approaches to enhance deep learning networks and achieve accurate segmentation of specific cardiac regions. These approaches include incorporating local modules, optimizing loss functions, and enhancing network architectures. A comprehensive summary of the aforementioned methods is presented, considering three imaging modalities: cardiac magnetic resonance imaging, computed tomography, and ultrasonic cardiogram. Lastly, the article concludes by summarizing the current research status and discussing research directions for further exploration.