Radio frequency receivers are critical to the quality of magnetic resonance imaging (MRI). To address the key challenge in receiver design—namely, that the high sampling rate required for analog-to-digital converters (ADC) leads to high power consumption and hardware complexity—this paper proposes a signal reception scheme based on high bit width and low sampling rate. By enhancing the quantization dynamic range, this approach reduces the reliance on high sampling rates, thereby significantly lowering system power consumption. At the digital signal processing level, the lower sampling rate offers greater flexibility for filter design, enabling excellent filtering performance with lower-order filters and reduced hardware resource requirements. Guided by the RF direct bandpass sampling theory, a low-power and low-sampling-rate receiver for 0.5 T MRI systems was designed in this study. Experimental results show that the proposed design improves signal dynamic range while ensuring MRI signal quality, and simultaneously achieves significant reductions in power consumption, hardware complexity, and system cost.

Halbach permanent magnets hold significant promise for low-field nuclear magnetic resonance (LF-NMR) applications, such as rock core analysis, owing to their yoke-free design and low external stray fields. Compared with small rock cores, large rock cores better preserve the original internal structure and fluid distribution, yet they demand a larger homogeneous region. However, the complex structure of Halbach magnets inherently yields inadequate initial homogeneity, making it difficult to directly obtain a sufficiently large homogeneous region. In this study, we defined a 100-mm-diameter spherical region of interest (ROI) and optimized the magnet structure using Halbach magnet theory and finite-element simulations. The final designed magnet provides a field strength of 158.4 mT and an initial homogeneity of 22 502 ppm (1 ppm=10^-6). We applied an improved harmonic-based passive shimming method and enhanced the field homogeneity to 1 496 ppm. After passive shimming, we acquired ¹H free induction decay (FID) signals from an aqueous CuSO₄ sample (Φ 100 mm × H 100 mm), and Carr-Purcell-Meiboom-Gill (CPMG) signals from a two-component CuSO₄ aqueous solution, and distinguished different samples based on their T₂. These results demonstrate that the proposed magnet design and passive shimming method are effective for constructing large-bore, high homogeneity Halbach magnets.

The widespread application of low-field permanent magnet NMR systems in areas such as food testing and material analysis places higher demands on the quality of NMR signals. Magnetic field uniformity is crucial for obtaining high-quality signals, and the performance of the shimming current amplifier directly determines the effectiveness of active shimming. This paper presents a specialized high-stability, low-ripple shimming current amplifier design for high-resolution, low-field permanent magnet NMR systems. The design employs a highly integrated modular architecture, supports multi-channel expansion via controller area network (CAN) communication, and utilizes a self-biased push-pull circuit to achieve bipolar current output from -1 A to +1 A, significantly improving output linearity. To ensure accuracy and stability, differential sensing is applied in the current feedback loop to effectively suppress common-mode noise. The amplifier demonstrates a linear regression coefficient R² > 0.999 999 9 and a 48-hour long-term stability of 5.3 ppm (1 ppm=10^-6), enabling precise current control with excellent linearity and stability, thereby meeting the driving requirements of shim coils in high-resolution, low-field NMR systems.

Specific imaging of the tumor microenvironment is critical for precise cancer diagnosis, yet conventional single-modal imaging techniques struggle to combine high sensitivity with high spatial resolution. This study designed and synthesized a cRGD peptide-targeted, nitroreductase (NTR)-responsive hemicyanine-¹⁹F dual-modal probe, Cy-F-cRGD. Through systematic screening of different fluorine-containing groups, we found that an ortho-difluoro structure induced a remarkable ¹⁹F chemical shift change (Δδ_F = 10.87). Concurrently, the hemicyanine unit exhibited an 80 nm red shift in absorption and a 90-fold fluorescence enhancement, achieving an optical “off-on” signal switch. This work offers a new design strategy for developing smart responsive multimodal probes.

Deformable registration plays a significant role in multiple medical image analysis tasks. However, the fixed receptive field of convolutional neural networks makes it difficult to fully capture the spatial context information of the brain. While the Transformer architecture can effectively capture global information, it suffers from high computational cost. To address this dilemma, we propose a deformable registration network based on multi-scale dilated residual convolution and dual attention. This network employs a multi-scale dilated residual convolution to capture local details and extensive context information simultaneously. A dual attention module is integrated to enhance feature representation capability. The decoder section introduces dynamic upsampling to precisely reconstruct high-frequency details, thereby ensuring the topological integrity of the deformation field. Experiments were conducted on two public brain datasets. The results demonstrate that the proposed model achieves high registration performance across multiple metrics, including Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and percentage of non-positive Jacobian determinant, indicating superior registration precision and smoother deformation fields.

Low-field nuclear magnetic resonance (LF-NMR) relaxation time spectrum analysis has been widely applied in petroleum engineering and geological exploration. However, conventional NMR relaxation time spectrum inversion algorithms require time-domain signals with high signal-to-noise ratios (SNRs), which are often difficult to obtain in practical scenarios such as on-site core analysis. To achieve accurate and stable inversion of low-SNR NMR signals, this paper proposes a deep learning-based inversion network named UDMCA (U-Net Denoising and Multi-scale Cross Attention Inversion Network), which incorporates a multi-scale cross-attention mechanism to extract and fuse relaxation features at multiple scales according to the decay characteristics of NMR signals, thereby improving inversion accuracy for low-SNR multi-component signals. In addition, a U-Net denoising module is introduced to further enhance noise robustness. Experimental results show that the proposed method achieves accurate and stable inversion for low-SNR signals and demonstrates good applicability in core NMR signal inversion, providing an effective solution for low-SNR LF-NMR data processing and inversion.

Multi-contrast MRI allows for the simultaneous acquisition of multiple weighted images, which improves imaging efficiency and provides rich quantitative information. However, reconstructing these images under high undersampling while ensuring anatomical consistency and physical plausibility remains a significant challenge. Existing methods often rely on scarce fully-sampled data or suffer from performance degradation due to domain shift. To address this, we propose a physics-prior-guided diffusion model that encodes MR signal evolution via Bloch equations into a dictionary-matching constraint. This constraint is directly coupled into the reverse sampling process, enabling physical correction of data-driven priors without retraining. Experimental results demonstrate superior generalization performance over supervised and self-supervised approaches, while the estimated parameter maps validate its high quantitative accuracy, highlighting its potential for clinical multi-contrast imaging.

Ensifentrine was approved for marketing by the U.S. Food and Drug Administration (FDA) in June 2024. To date, however, the literature has provided only the raw NMR spectral data of this drug without detailed signal assignments, which hinders impurity identification and quality control for ensifentrine. Furthermore, the compound contains multiple quaternary carbon and nitrogen atoms, presenting challenges in assigning NMR signals. In this study, we employed a Bruker Avance III 400 MHz NMR spectrometer to acquire ¹H, ¹³C, and two-dimensional NMR spectra of ensifentrine, and fully assigned all ¹H and ¹³C signals. Notably, unlike previous reports, we utilized DMSO-d₆ as the solvent, which enhanced sample solubility, permitted unambiguous identification of the coupling relationships of active hydrogen signals, and yielded well-resolved two-dimensional NMR spectra. This work provides a reference for NMR-based structural analysis, content determination, and quality control of the active pharmaceutical ingredient (API) ensifentrine.

Pulsed dipolar electron paramagnetic resonance (PD-EPR) is a powerful biophysical technique for probing the dynamic structures and interactions of biomacromolecules such as proteins. By measuring dipolar interactions between unpaired electrons, PD-EPR directly yields nanometer-scale distance distributions between electron spins, from which conformational transitions can be inferred by comparison under different experimental conditions. This review summarizes three major PD-EPR techniques, including double electron-electron resonance, double quantum coherence, and relaxation-induced dipolar modulation enhancement, together with commonly used spin labels and protein spin-labeling strategies, highlighting recent advances in the study of biomacromolecules and complexes.

Diffusion magnetic resonance imaging (dMRI) serves as an essential technique for imaging brain microstructure, exhibiting unique superiority in visualizing white matter fiber tracts. During the acquisition of diffusion-weighted images, multiple factors including signal attenuation, long echo time, and system noise lead to low signal-to-noise ratios (SNRs). This defect impairs the estimation of microstructure-related diffusion parameters, highlighting the importance of effective denoising for improving image quality and quantitative accuracy. This paper firstly introduces the fundamental imaging principles of dMRI and its noise statistical characteristics. Subsequently, it systematically reviews research progress in dMRI denoising methods, with a focus on deep learning-based approaches. The advantages and limitations of these methods are analyzed by evaluating their preservation of microstructural features during denoising. Common denoising evaluation metrics are summarized and compared with the performance of classical denoising methods. Finally, key challenges in current dMRI denoising research are summarized, and future directions are discussed.