Deuterium (²H) magnetic resonance imaging is an emerging molecular metabolic imaging method that can visualize the metabolic pathway in vivo, and therefore has great potential in clinical applications such as cancer detection. In this work, we aimed to compare the metabolic characteristics of three different deuterium-labeled glucose, namely [6,6’-²H₂]-glucose, [2,3,4,6,6’-²H₅]-glucose, and [1,2,3,4,5,6,6’-²H₇]-glucose through glioma cell experiment. The rat glioma C6 cells were incubated with three deuterium-labeled glucose, and cell media samples were collected at different time points, and underwent magnetic resonance deuterium spectroscopy to obtain the glucose consumption and the production of downstream metabolites, such as water and lactate, at each time point. The results showed that all three kinds of deuterium-labeled glucose probes were able to demonstrate the characteristic of tumor metabolism, and there was no significant difference in the consumption rate of the three kinds of deuterium-labeled glucose probes, and the production of the deuterium-labeled water and deuterium-labeled lactate were consistent with the theoretical estimation. Therefore, this paper concludes that the cost-effective deuterium-labeled glucose probe [2,3,4,6,6’-²H₅]-glucose has great clinical translational values.

Mitochondria generate reactive oxygen species (ROS) during respiration. Low levels of ROS are conducive to signal transduction, whereas excessive accumulation of ROS can lead to protein oxidative modification. Cytochrome c (cyt c) is a multifunctional metalloprotein located in mitochondria. The oxidative modification of cyt c, especially the Met80 has been found to result in conformational change, but the mechanism is still unclear. In this study, the terminal methyl group of methionine on cytochrome c was selectively labeled with ¹³C, and the modification of the methionine in cytochrome c under oxidative environments was tracked by NMR. It was observed that under oxidative environments, the protein was first converted from reduced state to oxidized state, then oxidatively modified. The oxidative modification of Met80 occurred under relatively high content of ROS, but did not result in distinctive conformation transition. The result suggests that the protein has high activity to resist ROS damage, therefore, plays a regulatory role in inhibiting apoptosis.

Magnetic resonance (MR) effective transverse relaxation rate ($R_{2}^{*}$) technique has been widely applied for assessing hepatic iron concentration. However,$R_{2}^{*}$ mapping of iron-loaded liver can be severely degraded by noise. With the development of deep learning, deep neural networks have become effective tools for MR parameter mapping. In this study, a model-guided self-supervised deep neural network was designed for MR $R_{2}^{*}$ parameter mapping of iron-loaded liver. A novel loss function that integrated a noise-corrected physical model and an improved total variation model was used to train the network, which did not require reference $R_{2}^{*}$ parameter maps. Meanwhile, compared to the conventional parameter fitting methods, model-guided self-supervised deep learning method enabled accurate and efficient $R_{2}^{*}$ mapping of iron-loaded liver, suppressed the effect of noise, corrected the bias introduced by noise, and preserved the detailed structure of $R_{2}^{*}$ map.

This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed. Secondly, the classification model of DenseNet combined with transfer learning was constructed and fine-tuned, MCN and SCN were classified by 5-fold cross validation, and the proposed classification model was compared with AlexNet, VGG16, ResNet50 and other deep learning models. The classification model in this paper yielded the best recognition effect. The area under the ROC curve (AUC value), accuracy rate, recall rate and precision rate of the test set were 0.989, 0.943, 0.949 and 0.938 respectively. It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models, which can help doctors in clinical diagnosis, and save manpower and material resources. It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors.

The magnetic resonance image segmentation of glioma is significant in disease diagnosis, surgical planning, and determination of treatment plans such as radiotherapy. In response to the problem of low segmentation accuracy, inaccurate edge segmentation, and prone to false positives in existing brain tumor segmentation algorithms, an improved Unet model based on multi-head self-attention and deformable convolution is proposed. The model replaces the standard convolution of the original Unet framework with residual modules to prevent vanishing gradient during model training. Multi-head self-attention modules based on Transformer are added in the bottleneck layer to extract local features and global context information for better exploration of correlations between pixels. Deformable convolution is used at cross-layer connections to enhance the model's sensitivity to shape perception and improve the ability to extract tumor edge features. Experimental results show that the segmentation evaluation metrics of the proposed algorithm are higher than those of other comparative literature and models using the same dataset, with more precise segmentation of tumor edges. This indicates that the algorithm proposed in this paper is an effective automatic glioma segmentation algorithm.

To investigate the performance of Mask RCNN (region-based convolutional neural network) using T1WI-enhanced images and fusing with attention mechanism in recognizing acoustic neuroma and meningioma in cerebellopontine angle area, this paper retrospectively collected 116 cases of meningioma and 427 cases of acoustic neuroma confirmed by pathology or clinical diagnosis. 872 images of meningioma and 2 467 images of acoustic neuroma were adopted after image screening. These images were divided into the training set, validation set, and test set in a ratio of approximately 7:1.5:1.5. After preprocessing the images, we employed five models, namely Mask RCNN model with Resnet50, Resnet101, VGG19 as the backbone network, and Mask RCNN models Resnet101-CBAM and VGG19-CBAM fusing with the convolution attention mechanism, for the detection and lesion segmentation of acoustic neuromas and meningioma in cerebellopontine angle area. The model performance was evaluated with mean average precision (mAP) and mean average recall (mAR). The results in the test set showed that the convolution attention mechanism could improve model performance. VGG19-CBAM model outperformed other models with mAP of 0.932 in classification and 0.930 in segmentation. This indicates that Mask RCNN fusing with attention mechanism has a better performance in recognizing acoustic neuroma and meningioma in cerebellopontine angle area, and hence can improve clinical efficiency by providing a reference for the diagnosis and target area segmentation.

This paper integrates the attention mechanism and multi-scale residual convolution to construct a Unet network, aiming at improving the quality of magnetic resonance image (MRI) reconstructed from under-sampled k-space data. To enhance the feature representation ability of the network and prevent gradient disappearance and degradation during network training, multi-scale residual convolution was embedded in the encoding path of the Unet network to extract different scale feature information of MRI. Moreover, to accurately recover the detailed texture features of MRI, the convolution attention module was embedded in the jump connection part between the encoding and decoding path of the Unet network to respond to the key information, such as details and textures in different degrees. Experiments showed that the proposed network could effectively reconstruct high-quality MRIs with clear texture and without overlapping artifacts from the under-sampled k-space data.

Arterial tree region segmentation from medical images of the brain is an early step in the diagnosis and evaluation of many cerebrovascular diseases. Most of the existing region segmentation methods rely on manual assistance. In this paper, we propose an automatic brain arterial tree partitioning method based on a dual branch connected network (DBCNet), which can partition the arterial tree in time of flight-magnetic resonance angiography (TOF-MRA) into six main regions. The branch feature decoupling module and the global and local feature fusion module based on Swin Transformer mechanism were used for DBCNet. The two-step training strategy of localization followed by segmentation was used for training. In this study, 111 cases of TOF-MRA data were used, of which 81 cases as the training set, 20 cases as the validation set, and 10 cases as the test set. The average Dice coefficient of the model on the test set was 74.72% and 95% Haus dorff distance (HD95) was 3.89 mm. Compared with other advanced segmentation networks, the network reported in this paper can segment each major region more accurately with robustness.

Temperature drift is an important factor affecting the measurement accuracy of desktop NMR spectrometers, and adding a field-locking coil to the probe to achieve field-frequency interlocking is a common means of suppressing temperature drift. In this paper, a dual-channel miniaturised RF probe with an external field-locking channel is designed based on a laboratory compact Halbach magnet. The coil diameter, height, number of turns, turn spacing and enamelled wire radius were optimized based on the solenoid structure. The optimum solenoid size was obtained with an enameled wire radius of 0.4 mm, a coil diameter and height of 8.2 mm, a turn spacing of 1.6 mm and a number of turns of 5. Based on the simulation results, the detection and field-locking coils were fabricated, and tested in conjunction with the peripheral circuitry. The results show that the crosstalk between the two coils is low, the signal-to-noise ratio of the detection channel is above 50 and the signal-to-noise ratio of the locking channel is above 20. Final field locking experiments were performed and the frequency drift of the overall system after equipping the locking field was approximately 0.2 ppm/h (1 ppm=10^-6), verifying that this probe design can be applied in compact Halbach magnet-based NMR analysis facilities.

Electron paramagnetic resonance (EPR) is a measurement method to research the microscopic information of magnetic materials. Because early EPR studies were limited by magnetic field strength and microwave frequency, some microscopic information of materials could not be clearly displayed. In recent years, with the development of high magnetic field technology and microwave technology, continuous wave electron paramagnetic resonance (cw-EPR) spectrometer and pulsed electron paramagnetic resonance (pulsed EPR) spectrometer have been fully used under high frequency and high field, and the sensitivity, spectral resolution and other performance indicators of the spectrometer have also been improved. This paper mainly introduces the principle and structure of EPR spectrometer under high frequency and high field, its development history and research status both domestically and internationally, and the latest application in related fields.