The acute exacerbation of chronic obstructive pulmonary disease (AECOPD) constitutes a significant adverse event in the trajectory of COPD. In this study, we utilized hyperpolarized ¹²⁹Xe magnetic resonance (MR) to explore the alterations in alveolar microstructure and pulmonary gas exchange function in an AECOPD disease model. Our findings revealed that, in comparison to the emphysema group, the microstructure remained relatively unchanged in the acute exacerbation group, while gas exchange function exhibited a decrease. Notably, these observed changes correlated well with histological findings and pulmonary function assessments, indicating the feasibility and sensitivity of hyperpolarized ¹²⁹Xe MR in detecting AECOPD.

Glutathione (GSH), a tripeptide derived from glutamic acid, cysteine, and glycine, as a vital source of reducing power in the body, is crucial for maintaining redox balance and combating free radicals. Its role in tumor therapy is also significant. Monitoring GSH levels in vivo using magnetic resonance spectroscopy (MRS) is essential for understanding its biological functions. However, due to complex biological environments, overlapping problem of MRS signals is often serious, which makes selective detection and accurate quantification challenging. This paper introduces a method to selectively detect GSH in vivo by using nuclear spin singlet states. Compared to other methods in literatures, our method offers high efficiency and strong selectivity.

Dissolution dynamic nuclear polarization (dDNP) involves polarizing the sample under microwave irradiation at a low-temperature and strong-magnetic field, dissolving it by solvent of high-temperature and high-pressure, then injecting it rapidly into an NMR/MRI for detection. The dissolution system serves as a pivotal component in the dissolving and injecting process, with its performance directly affecting the melting and transfer efficiency and the high polarization retention. We present an automated dissolution system using a dual-loop proportional-integral-derivative (PID) control algorithm that can reach the target pressure of ~1.7 MPa after 9 min and control the internal pressure within 1.5%. The system can dissolve a solid sample of -271 ℃ in the self-developed 5 T dDNP polarizer and transfer it to the 7 T NMR system at a distance of 11.2 m for detection in 4 s, yielding a 9050-fold enhancement of ¹³C NMR signal of sodium [1-¹³C]-pyruvate at the solvated state. In summary, this work developed an automated dissolution system that simplifies the operation steps, ensures detection consistency, and improves experimental safety.

This paper presents a new W-band electron paramagnetic resonance (EPR) probe design based on Fabry-Perot cavity. In this probe, a Fabry-Perot cavity with open structure and large effective volume is used as a resonator. Corrugated waveguides are designed to achieve low-loss microwave signal transmission between the W-band microwave bridge and the Fabry-Perot cavity. The probe works in W-band, and the unloaded quality factor is 1 542. A room-temperature continuous wave EPR experiment with Mn(II) in CaO powder was conducted after assembling the probe into a W-band EPR spectrometer. The results show that the modulation field amplitude is 8.9 G (1 G=10^-4 T) when the modulation field frequency is set to 100 kHz, and the absolute spin sensitivity is estimated to be 6.6×10⁸ spins/(G•$\sqrt{Hz}$), which verifies the feasibility of utilizing Fabry-Perot cavity as a high sensitivity W-band EPR probe.

The emerging wearable magnetoencephalography technology lays the foundation for brain-computer interface to provide high-quality data. To explore the feasibility of applying wearable magnetoencephalography in visual hybrid brain-computer interface, a SSVEF-Alpha hybrid brain-computer interface is designed based on steady-state visual evoked field and Alpha wave, and the performance is compared with different classification models. The results show that based on the user-dependent training method, the average classification accuracy of hybrid brain-computer interface is (93.29±1.69)%, the information transmission rate can reach 86.81 bits/min. And the user-independent training method with short data length shows superiority over the training-free method. This study verifies the effectiveness of visual hybrid brain-computer interface and provides a reference example for further development and design of brain-computer interface products of wearable magnetoencephalography.

How to improve the speed of MRI is a standing problem in the field of magnetic resonance. A commonly used approach for acceleration is multi-coil scan. However, when the acceleration factor exceeds 4, the image quality obtained by traditional compressed sensing magnetic resonance imaging (CS-MRI) reconstruction algorithms becomes unsatisfactory. In this study, we propose a multi-coil MRI image reconstruction method named as ISTAVS algorithm based on physical model. It combines the ISTA algorithm with the splitting idea of VS-Net, and is expanded into an ISTAVS-Net. Each iteration step is combined with the network module, which has higher interpretability than black box U-Net. The residual mechanism is introduced into the ISTAVS-Net to increase the non-linear expression ability and accuracy. Sparse transformation, shrinkage threshold and regularization parameter are automatically learned during training, which increases the flexibility of reconstruction. The test results of the Globus knee dataset show that the ISTAVS-Net outperforms traditional L1-ESPIRiT and ISTA algorithm by multiple indicators, including the improvement of image quality and performance metrics over U-Net, ISTA-Net+ and VS-Net at different acceleration factors, and the ability to recover tissue details at high acceleration factors. The proposed network demonstrates good robustness and is more suitable for fast and high-quality reconstruction of data acquired on clinical MR scanners, thereby can expand the application range of MRI.

The fiber tracking algorithm can track the brain fibers through the fiber orientation distribution function. Considering that the dispersion of water molecules is mutual and the fiber orientation distribution function reconstructed from scanned data may have errors, this paper proposes a fiber tracking algorithm optimized by direction correction based on the traditional streamline tracking algorithm combined with the maximum cosine similarity. Meanwhile, considering the existence of anisotropically dispersed and isotropically dispersed water molecules in the human brain, and that the latter accounts for a larger proportion, the maximum expectation algorithm is used to cluster the seed points with the same properties to reduce the tracking of isotropically dispersed voxel points. Finally, the experiments were conducted using simulated and real data respectively, and the results show that the proposed algorithm takes less time for tracking, the average fiber length is longer compared to the traditional streamline tracking streamline tracking (STT) algorithm, the number of incorrectly tracked clusters is significantly less and the ratio of correctly tracked bundles significantly higher than that of the traditional fiber tracking algorithm. Additionally, it demonstrates a higher overlap rate and a lower overestimation rate in the tracking of most specific fiber bundles, better reflecting the structural distribution of fibers in practical scenarios.

In chemical exchange saturation transfer (CEST) imaging, the relaxation recovery of longitudinal magnetization during the signal acquisition process results in a decrease in CEST contrast. Consequently, the CEST saturation mode and the corresponding K-space filling method significantly influence both the image quality and quantitative results of CEST. In this paper, simulations and human experiments were conducted to examine the impact of various K-space filling strategies within the fast low angle shot (FLASH) sequence on the quality of brain amide proton transfer (APT) images and on the quantitative analysis of magnetization transfer ratio asymmetry (MTR_asym). The result indicates that employing segmented saturation techniques with optimized reordered center-out K-space filling technology can effectively mitigate artifacts caused by scalp fat and enhance the accuracy of MTR_asym values.

Spatio-temporal encoding (SPEN) magnetic resonance imaging (MRI) is an ultrafast MRI technique. However, resolution of the original image acquired with SPEN is relatively low, requiring super-resolution reconstruction based on sequence physics principles to improve spatial resolution. As the existing SPEN super-resolution reconstruction algorithms based on deep learning have confined abilities to capture long-range dependencies, this paper proposes a transformer-based SPEN MRI super-resolution reconstruction algorithm. An encoder-decoder structure is adopted, and a transformer module is introduced to extract local context information and long-range dependencies of feature maps. Experimental results show that the proposed reconstruction method can reconstruct a super-resolution image with high spatial resolution and no aliasing artifacts from the low-resolution SPEN image without adding additional sampling points. Compared to the existing super-resolution methods, the proposed method achieves better results on both clinical and preclinical datasets.

Vapor cell’s helium permeation is considered to be one of the reasons for the frequency drift of rubidium (Rb) clocks. In this study, we measured the helium permeability for both commonly used Pyrex Rb cell and newly developed anti-helium aluminosilicate Rb cell. Their helium permeabilities at operating temperatures (~60 ℃) are 2E-19 m²/(Pa•s) and ≤3E-22 m²/(Pa•s), respectively. The analysis shows that the Pyrex Rb cells causes clock drift from helium permeation to be about E-13/day in the first four years and E-14/day in the next six years, and the aluminosilicate Rb cell can suppress the drift from helium permeation to E-16/day. The results are instructive for improving Rb clock’s long-term frequency drift performance.