Applied Magnetic Resonance最新文献

The quest to enhance the sensitivity of electron spin resonance (ESR) is an ongoing challenge. One potential strategy involves increasing the frequency, for instance, moving from Q-band (approximately 35 GHz) to W-band (approximately 94 GHz). However, this shift typically results in higher transmission and switching losses, as well as increased noise in signal amplifiers. In this work, we address these shortcomings by employing a W-band probehead integrated with a cryogenic low-noise amplifier (LNA) and a microresonator. This configuration allows us to position the LNA close to the resonator, thereby amplifying the acquired ESR signal with minimal losses. Furthermore, when operated at cryogenic temperatures, the LNA exhibits unparalleled noise levels that are significantly lower than those of conventional room temperature LNAs. We detail the novel probehead design and provide some experimental results at room temperature as well as cryogenic temperatures for representative paramagnetic samples. We find, for example, that spin sensitivity of ~ 3 × 10⁵ spins/√Hz is achieved for a sample of phosphorus doped ²⁸Si, even for sub-optimal sample geometry with potential improvement to < 10³ spins/√Hz in more optimal scenarios.

The negatively charged boron vacancy (({text{V}}_{text{B}}^{-})) in hexagonal boron nitride (hBN) is currently considered an intriguing quantum object for testing and developing quantum technologies on two-dimensional van der Waals materials. This article presents results from photoinduced electron spin echo (ESE)-detected electron spin resonance (ESR) and electron–nuclear double-resonance (ENDOR) spectroscopy at the W-band (ν = 94 GHz), focusing on the interactions of the ({text{V}}_{text{B}}^{-}) electron spin with the three nearest nitrogen nuclei (¹⁴N, I = 1). The lines in the ENDOR spectrum are due to both hyperfine and quadrupole interactions for M_S = ± 1 levels and only quadrupole interactions for M_S = 0 levels. We show that significant hyperfine interaction with the three nearest nitrogen atoms, despite the high magnetic field, results in a mixing of the hyperfine sublevels for M_S = 0. We show that significant hyperfine interaction with the three nearest nitrogen atoms, despite the high magnetic field, results in mixing of the hyperfine sublevels. This mixing shifts the ¹⁴N Larmor frequency from its nominal value defined as ({{varvec{nu}}}_{{varvec{L}}}=boldsymbol{ }{{varvec{g}}}_{{varvec{N}}}{{varvec{mu}}}_{{varvec{N}}}{varvec{B}}/{varvec{h}}). This shift observed through ENDOR experiments can be understood using spin-Hamiltonian formalism within the second-order perturbation theory. These findings enhance an understanding of electron–nuclear interactions in hBN.

Skull stripping is a fundamental step in analyzing magnetic resonance imaging (MRI) scans, which play a crucial role in disease diagnosis such as Alzheimer’s disease (AD). Alzheimer’s is a progressive neurological disorder with no known cure. Early and precise diagnosis of AD is essential for timely intervention to help slow its progression. Although manual brain segmentation from MRI is accurate, it requires expert knowledge, experience, and time investment. Therefore, many automated brain segmentation algorithms have been introduced so far. The U-Net model has recently gained significant attention due to its exceptional volumetric medical image segmentation performance. This study presents a novel multidimensional multi-input multi-output U-Net (MIMO-U-Net) model for more efficient brain extraction. The model is multidimensional because it works with both 2D and 3D datasets. This architecture uses a dropout regularization technique with varying dropout rates across different layers. The concatenation connections are also used to combine high-level features with up-sampled output. The dropout regularization and concatenation help in enhancing the model performance. A refined loss function is also proposed by combining Dice loss and categorical focal loss. The MIMO-U-Net is trained and tested using a T1-weighted ADNI brain MRI dataset. The results indicate that MIMO-U-Net surpasses most existing techniques by offering better accuracy and notable quantitative and qualitative outcomes. In addition, the MIMO-U-Net showcases substantial computational efficiency during execution. Evaluation metrics, comprising the Dice coefficient, specificity, and sensitivity, corroborate the model’s performance with precise scores of 0.992, 0.999, and 0.995, respectively.

Brain Tumor (BT) is the most serious illness affecting humans, and its diagnosis is a complex process. Tumor location and type significantly affect treatment decisions, and survival rates improve with accurate identification and classification in the early stages. Magnetic Resonance Imaging (MRI) is mainly used for brain tumor analysis, but manual detection and classification by clinicians is challenging, often leading to high error rates, inaccurate diagnoses, and prolonged time requirements. To overcome these challenges, this paper introduces a novel hybrid classification approach that combines Capsule Networks (CapsNet) and XGBoost (XGB) to classify brain tumors from MRI images. The preprocessing step includes normalization, image blurring, resizing, contrast enhancement, and noise elimination, which are used to improve image quality. The classification process employs CapsNet to capture hierarchical features and spatial relationships in the images, while XGB utilizes extracted features, such as texture, intensity, and shape, to classify tumors effectively. To improve diagnostic accuracy, a Meta Ensemble Model combines the predictions of both algorithms using a Weighted Majority Voting approach, adjusting contributions based on each model’s confidence. Additionally, the Mantis Search Algorithm (MSA) is utilized for hyperparameter tuning, optimizing model performance by exploring the hyperparameter space effectively. The experiment assessed using the Brain Tumor MRI Dataset and Figshare Brain Tumor Dataset demonstrates the effectiveness of the proposed method, achieving an accuracy of 99.34% and a precision of 98.82%. These results indicate that the hybrid method is highly effective in accurately classifying various brain tumor types, which provides the best solution for clinical diagnostics.

Ionic liquids (ILs) are salts which persist in liquid state near room temperature. They are characterized by high thermal and chemical resistance, good solubility, and high ionic conductivity. ILs can be used as permeability enhancers for transdermal delivery of drugs. The study of the interaction of ILs with lipids is important for understanding their potential toxicity to cells and the environment. In this work, we discuss features of the molecular structure and mobility of the aqueous system consisting of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and ethylammonium nitrate ionic liquid (EAN) mixtures studied by NMR and computer simulation MD methods. The ³¹P NMR line shape analysis revealed two lipid states in the systems: in D₂O it is a lamellar liquid crystalline state associated with the formed vesicle-like structures of DMPC, while in EAN it is isotopic. The ratio of these states correlates with the ratio of solvents in the system. Based on the self-diffusion coefficients obtained by NMR, sizes of the diffusing particles were estimated. The method of MD showed that DMPC molecules assemble into micelles in the presence of water. In the mixtures of EAN and water the configuration of DMPC molecules changed. When DMPC interacts only with EAN, the micelle disintegrates. It is thus inferred that the presence of IL in the environment significantly affects the structure of the lipid system. The comparative analysis of the SDCs revealed a correlation between values obtained by MD and NMR methods.

EPR applications have evolved continuously with the development of the EPR technique. Since 1967, Bruker EPR is constantly developing its products to keep pace with these constantly changing demands. This article presents commercial EPR solutions in perspective of the past, present, and future.

Sepsis-associated encephalopathy (SAE) results in widespread brain dysfunction due to systemic infection without direct central nervous system involvement. This study aimed to assess functional and metabolic alterations in a rat model of SAE using multi-parametric magnetic resonance imaging (mpMRI). Twenty-one rats were divided into control (CTRL, n = 7), SAE05 (5 mg/kg LPS, n = 7), and SAE10 (10 mg/kg LPS, n = 7) groups. MPI was performed 24 h post-LPS injection using a 7 T MR system to capture apparent diffusion coefficient (ADC), cerebral blood flow (CBF), T1, and T2 maps. In our study, the ADC and T1 values were significantly elevated in both the left and right hippocampi of the SAE05 and SAE10 groups compared to the CTRL group. However, the CBF and T2 values did not exhibit significant changes across the groups. This study utilized multi-parametric MRI to evaluate functional and metabolic changes in the brain associated with SAE. The results revealed significant increases in ADC and T1 values, indicating vasogenic edema and blood–brain barrier disruption in the hippocampus, which provide crucial insights for diagnosing and developing treatments for inflammatory brain diseases.

This work addresses the development of a custom-made home-built rapid freeze–quench (RFQ) device and the comparison of its performance to the one of a commercial RFQ setup that was in-house custom adapted. Both systems consist of two syringes that push the reactants into a mixing chamber and the products to a subsequent freezing setup. Using the binding of azide to myoglobin as a calibration reaction, the quenching times of the different setups were compared, evaluating different instrumental parameters, such as software-controlled variation of the aging time, variations of the flow rate and variations of the distance travelled by the mixed sample before freezing. In addition to minimal sample consumption, the home-built RFQ device was found to lead to the shorter reaction times which could be controlled in a time range from 10 to 25 ms. The commercial RFQ system yielded optimal reaction control in a time range from 50 to 200 ms, although a larger volume of reactants needed to be used due to the significant dead volume of the system. Three different freezing methods were also evaluated, among which, in our hands, freezing the jet directly in a deep bath of cold isopentane yielded shorter and reproducible freezing times.