Based on the independently developed 5 T magnetic resonance imaging (MRI) system and research needs of ¹³C metabolic MRI, a gradient coil and ¹H/¹³C dual resonance radiofrequency (RF) coil system for small-bore MRI were designed. The gradient coil was designed by using the finite-difference stream function method, and the RF coil was designed as a saddle coil combined with a surface coil in a dual-resonance scheme. Numerical simulation analysis of the magnetic field distribution was carried out by using the finite-element method, and a set of gradient and RF coils for small-bore 5 T ¹³C MRI was successfully developed. The feasibility of the design scheme was verified using a home-made 5 T MRI system, and the magnetic resonance images of ¹³C-labeled urea phantom and ¹H magnetic resonance images of mice head were acquired by experimental tests, which lays a foundation for ¹³C metabolic MRI based on dynamic nuclear polarization in the future.

Conventional nuclear magnetic resonance (NMR) instruments are large and difficult to carry, which limits their application in fields such as on-site petroleum exploration, food safety, environmental pollution, and quality control. To address this issue, this paper proposes a design of a handheld NMR console. By utilizing an advanced RISC machine (ARM) core construction, field programmable gate array (FPGA) logic design and control program design on a programmable system-on-chip Zynq-7000, the entire handheld NMR console was designed and implemented. After completing the entire design, several basic pulse sequences, including free induction decay (FID), spin echo (SE), and Carr-Purcel1-Meiboom-Gill (CPMG), were tested on a 0.5 T desktop NMR system developed by our research group. The tests validated the correctness of the overall architecture design and the coordination among various modules. The designed NMR console is 10.6 cm long, 6.0 cm wide, and 1.9 cm high, which significantly reduces the size while enhancing the real-time performance of pulse sequence and improving the stability of the console, laying a foundation for further development of portable NMR instruments.

The application of hyperpolarized ³He gas is extensive in fields such as neutron polarimetry, fundamental physics, and diagnostic medicine. The polarimetry measurement is crucial for ³He polarization systems. Typically, a free induction decay nuclear magnetic resonance (FID NMR) coil is used to detect the FID signal of ³He atoms. The measurement signal-to-noise ratio (SNR) of the coil is correlated with factors such as diameter, number of turns, and relative position to the cell. There is limited literature and insufficient experimental verification on the optimization analysis of FID NMR coils. In this study, an SNR model for the coil was established through theoretical analysis of the FID signal and coil noise. The FID NMR coil was experimentally validated in a ³He polarization system based on the metastability-exchange optical pumping (MEOP) technique, which is known for its high polarization efficiency. The experimental results indicated that for a cylindrical cell with both radius and height of r_cell, the optimal SNR of the coil was achieved at a radius of r_cell/2, which was consistent with the theoretical analysis. The relative error of the fitted SNR model was less than 10%, demonstrating its effectiveness. These research findings hold significant practical implications for optimizing the design of ³He polarization systems.

This study investigates the correlation between ¹²⁹Xe ventilation functional MRI and CT-detected pulmonary lesion types. We performed ¹²⁹Xe ventilation functional MRI and CT scans on a sample of 143 patients, subsequently analyzing the relationship between lung ventilation function at the lobe level and CT lesion types. The findings suggest that the ventilation function from ¹²⁹Xe MRI was consistent with CT results in the majority of lung lobes (74.6%). The ventilation function impairment of lung correlates with CT lesion types, particularly in instances of mixed diseases and emphysema/bullae, which are more prone to induce lung ventilation dysfunction. Moreover, distinct ventilation patterns are observed among different lesion types. That is, the data from both ¹²⁹Xe ventilation functional MRI and CT provide complementary insights. The preliminary finding of this study offers data support for evaluating lung ventilation function caused by lung lesions, and provides more references for clinical imaging diagnosis, thereby contributing to the refinement of diagnostic and therapeutic strategies for pulmonary diseases.

Existing research commonly uses functional connectivity (FC) combined with graph theory analysis to accomplish the auxiliary diagnosis of mild cognitive impairment (MCI). Traditional FC analysis methods usually target low-order FC networks, while high-order FC networks can reveal higher-level interactions in brain networks. However, there are few studies involving graph theory in high-order FC networks, and traditional graph theory indicators have limitations in high-order FC networks. This paper constructs a high-order FC network through high-order dynamic functional connections, combines graph theory to analyze the brain network status of MCI and normal cognition (NC), and defines two new graph theory indicators, blocking coefficient and average transition time, to characterize temporal variability in brain networks. The results show that the application of graph theory in high-order FC network can effectively extract the differential information between MCI group and NC group. The proposed blocking coefficient and average conversion time index can both show significant differences, providing a new analysis method for the study of high-order brain network.

In order to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context, this study designed a related task paradigm and applied a new generation magnetoencephalogram combined with the machine learning model to analyze the collected signals from three perspectives: behavioral response, event-related field (ERF) and single-trial detection. Results show that the unimodal semantic response was mainly concentrated in the occipital cortex, while the bimodal semantic response was mainly concentrated in the parietal cortex. At the same time, respondents' response rate and the detection accuracy of single-trial in bimodal mode were significantly higher than that in unimodal mode. Moreover, the support vector machine (SVM) showed the best classification performance among the four machine learning models, with an average classification accuracy of 75.16% for within-subject classification and 80.56% for between-subject classification. This research concludes that the combination of optically pumped magnetometer-magnetoencephalography (OPM-MEG) and machine learning model provides an efficient approach to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context.

¹H NMR spectral features of hydrogen molecules in the stacking interstices of silicon dioxide (SiO₂) microspheres with different sizes are studied in the present article. The chemical shift of hydrogen molecules is observed to gradually shift towards higher fields as the size of the stacking interstices decreases. Combined with experimental results of variable temperature hydrogen spectroscopy, self-diffusion coefficients measurement, and scanning electron microscopy, the observed phenomenon is attributed to two factors, namely, 1) the microsphere size-dependent local magnetic field inhomogeneity in the stacking interstices caused by the diamagnetic SiO₂ microspheres; and 2) the rapid exchange among hydrogen molecules experiencing different local fields within the microsphere interstices. The results of this study demonstrate the potential of hydrogen gas as a probe molecule for measuring micro- and nano-scale pore sizes.

Research on the stereochemistry of natural products, especially that of flexible macrocyclic molecules, has always been a huge challenge. Cembranes diterpenoids are a typical class of flexible macrocyclic molecules, whose configurations are often determined by coupling constant and nuclear Overhauser effect (NOE), both of which are nuclear magnetic resonance (NMR) parameters with local character. The development of methods such as residual dipolar coupling (RDC) and quantum chemical calculations has further expanded the toolbox for solving stereochemical problems. RDC, as an NMR parameter with non-local character, has not only been reported in the structure construction and function research of biomolecules such as proteins and nucleic acids, but has also gradually been applied to the configuration determination of natural products, contributing significantly to the stereochemical research on drug molecules. At present, there are few reports of RDC application in determining the configurations of cembranes diterpenoids. Here, the stereochemical configuration of sinulariol Z is investigated by RDC for the first time and confirmed by X-ray diffraction, further demonstrating the feasibility of RDC in the stereochemical study on cembranes diterpenoids.

Due to the collision-shift effect, helium permeation into the vapor cell causes a time-dependent frequency drift in rubidium atomic clock. To quantify this shift, a cylindrical vapor cell with dimensions of 1.8 cm in diameter, 1.6 cm in height, and 1 mm in thickness, operating at a temperature of 65 ℃, is selected for analysis. Numerical methods are employed to simulate the temporal variation of helium gas pressure within atomic vapor cells composed of Pyrex (Corning 7740) and low helium-permeable aluminosilicate (Corning 1720). The results of the calculations indicate that for Pyrex vapor cells, after 12 years of operation, the drift rate attributable to helium permeation decreases to less than 1.0×10^-14/day. In contrast, the frequency drift rate due to helium permeation in aluminosilicate vapor cell rubidium atomical clocks remains below 3.0×10^-17/day throughout their operational lifetime, rendering its contribution to the drift rate negligible. This computational approach is also applicable to the investigation of the permeation processes of other gaseous species in various glass materials.

Diffusion tensor imaging is an essential technique to study tissue brain microstructure and the distribution of white matter fiber tracts. However, affected by the diffusion-weighted signal attenuation and long echo time, diffusion tensor images suffer from serious low signal-to-noise ratio problem. Therefore, efficient denoising techniques are crucial for enhancing image quality. This paper starts with the principle of diffusion tensor imaging and the types of noise. Then it discusses the classical diffusion tensor image denoising algorithms, including algorithms based on traditional image processing and deep learning. Special emphasis is given to the status and shortcomings of diffusion tensor image denoising research. The denoising evaluation criteria and commonly used public datasets are also introduced, followed by experiments and quantitative analysis on the diffusion tensor image denoising methods mentioned in this paper. Finally, it concludes with a summary and an outlook for the field’s future research directions.