Magnetic resonance imaging (MRI) can quantify characteristic values of tissues, serving as an important tool for scientific and clinical research. Magnetic resonance T_1ρ relaxation time reflects the low-frequency motional processes between water and macromolecules. At high fields of 3 T and above, T_1ρ is greatly affected by the chemical exchange between water and exchangeable protons, and T_1ρ dispersion measured with varying spin-lock fields can be utilized to analyze and quantify the proton exchange process. However, it is time-consuming to obtain T_1ρ-weighted images with different spin-lock fields, which limits its application. To solve this problem, a fast T_1ρ dispersion imaging method based on multiple relaxation signal compensation strategy is proposed in this work, which compensates the T_1ρ-weighted images at different locking frequencies to the same signal strength level, and combines the low-rank plus sparse model in the reconstruction. Experimental results show that the proposed method achieves good reconstruction results even when the acceleration factor is up to 7.

Internet gaming disorder (IGD) has a great negative impact on teenagers’ study and life. It has been included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders in 2013. However, the neural mechanism of its effect is yet to be unveiled. In this study, we used resting-state functional magnetic resonance imaging (resting-state fMRI) to explore the differences of rich club structures in the functional brain networks of 30 IGD subjects and 30 age/sex-matched healthy controls. The rich club was found in both IGD subjects and healthy controls, involving important brain regions in default mode, executive control, salience, sensorimotor, auditory and visual networks. IGD subjects had significantly higher rich club connection and higher degree in the right pars orbitalis of inferior frontal gyrus than healthy controls. These findings might suggest that IGD might be more correlated with damage to the rich club connection.

Subarachnoid hemorrhage caused by the rupture of cerebral aneurysms is extremely fatal and disabling. It’s imperative for radiologists to achieve efficient screening with the help of deep learning-based models. To improve the detection sensitivity of time of flight-magnetic resonance angiography (TOF-MRA) images, this study proposed a neural network named DCAU-Net which is based on fuzzy labels, 3D U-Net variant, and dual-branch channel attention (DCA), and able to adaptively adjust the response of channel features to improve feature extraction capability. First, TOF-MRA images from 260 subjects were preprocessed, and the data were split into the training set (N=174), validation set (N=43) and testing set (N=43). Then the preprocessed data were used for training and validating DCAU-Net. The results show that DCAU-Net scores 90.69% of sensitivity, 0.83 per case of false positive count and 0.52 of positive predicted value in the testing set, providing a promising tool for detecting cerebral aneurysms.

Liver cancer is one of the most common malignant tumors. In Asia, liver cancer often develops on a background of cirrhosis caused by chronic hepatitis. The procedure of hepatitis, cirrhotic nodules, dysplastic nodules, and then hepatocellular carcinoma is the most common liver cancer evolutionary process. Judging the stage of hepatic nodules in the evolution process and taking intervention measures are critical for reducing the incidence of liver cancer. In this paper, a more accurate support vector machine (SVM) classification algorithm, LFOA-F-SVM, was proposed for radiomics to classify hepatic nodules from 120 patients into four categories based on dynamic enhanced magnetic resonance images. The algorithm uses radius-margin-based F-SVM, and combines the fruit fly optimization algorithm (FOA) of Levy flight (LF) strategy to optimize the parameters. To verify the effectiveness of the method, five UCI classification data sets (hearts, Parkinson’s disease, iris, wine and zoo) were added and compared with SVM, PSO-SVM, FOA-SVM, F-SVM. The results showed that LFOA-F-SVM has the highest classification accuracy in six data sets compared to the other methods. And in the hepatic nodules data set, the classification precision and recall are relatively high.

To address the low precision of pairwise registration method based on the deep learning and the time-consuming nature of traditional registration algorithm, this paper presents a method of unsupervised end-to-end groupwise registration based on variational inference, as well as a registration framework based on normalized cross correlation (NCC) and prior knowledge. The framework can warp all images in the group into a common space and effectively control the deformation field of the regularization, and it doesn't need a real deformation field or a reference image. The estimation of deformation field by this method can be modeled as a probability generation model and solved by variational inference. Then unsupervised training is implemented with the help of spatial transformer network and loss function. The registration results of 3D brain magnetic resonance image from the public data set LPBA40 show that: compared with the baseline method, the proposed method has better Dice score, less running time, better diffeomorphisms domain, and is robust to noise.

Knee joint is commonly hurt by rheumatoid arthritis (RA). Accurate segmentation of synovium is essential for the diagnosis and treatment of RA. This paper proposes an algorithm based on improved VNet for automatically segmenting knee joint synovial magnetic resonance images. Firstly, the knee joint magnetic resonance images of 39 patients with synovitis were preprocessed. VNetTrans was constructed by embedding Transformer at the bottom of VNet. The MemSwish activation function was used for training. The average Dice score of the final model is 0.758 5 and the HD is 24.6 mm. Compared with VNet, the proposed model increased Dice score by 0.083 6 and decreased HD by 10 mm. Experimental results demonstrated that the proposed algorithm achieved satisfying 3D segmentation of the synovial hyperplasia area in the knee magnetic resonance images. It can be utilized to facilitate the diagnosis and monitoring of RA.

Due to the detection sensitivity provided by the high nuclear spin polarization, hyperpolarized ¹²⁹Xe has been employed in animal and human magnetic resonance imaging (MRI). However, during the accumulation-thawing process of hyperpolarized ¹²⁹Xe, multiple factors lead to the relaxation of spin polarization and would hinder the wider application of ¹²⁹Xe. In this research, the spin relaxation mechanism of hyperpolarized ¹²⁹Xe during the accumulation-thawing process is investigated by both theoretical model analysis and experimental measurements. Meanwhile, the stability of the homebuilt device for the accumulation-thawing is measured. Our results demonstrate that the thawing mode and the cold trap material significantly affect the polarization loss; the automatic device is very stable during long-term operation and shows a high degree of automation, resulting in a polarization recovery ratio of 85.6% ± 4.7%. This research greatly helps to improve the efficiency of hyperpolarized ¹²⁹Xe in animal and human MRI.

In this article, an effective method to keep the phase coherence between the transmitter and the receiver in digital magnetic resonance (MR) console is proposed. Relying on programmable digital logic, we design two digital reference sources with the same frequency and embed them in the transmitter and the receiver respectively. During pulse sequence execution, when the transmitter or receiver loses phase coherence due to frequency switching, the phase coherence is restored by resetting the phase of the transmitter and receiver to ensure they are synchronized with their corresponding reference source. The experimental results have verified that this method can accurately keep the phase coherence between the transmitter and receiver, and neither additional hardware circuit nor complex sequence design is required, which adds to great practicability.

Magnetoencephalography is a non-invasive technology for brain function imaging, which is of enormous value to brain science research and clinical application due to its ultra-high temporal and spatial trace resolution. In this paper, we introduce a self-built and atomic magnetometer based wearable magnetoencephalography system. By designing bi-planar coils system and combining with reference sensor array, the residual magnetic field in the subject's head movement area is controlled to be within ±1 nT, which ensures the sensors are maintained within their dynamic range during the moving measurement. At the same time, a virtual gradiometer-based noise reduction method is proposed to suppress the common-mode magnetic-field noise. Finally, the alpha rhythm and auditory evoked magnetic field signals with high signal-to-noise ratio are successfully detected under the subject's natural head movement and the effectiveness of the system is confirmed. This study could provide more possibilities for the application and promotion of moving wearable magnetoencephalography.

Magnetic resonance (MR) plays an essential role in chemical analysis and medical imaging. The reliable MR instrument is a prerequisite for MR related research. Foreign instrument companies have monopolized the MR instrument supply in China for a long time. Recently, the situation has witnessed impressive improvement due to progress made by Chinese domestic companies and institutions in developing and manufacturing MR instruments. This paper surveyed and reviewed the status and challenges of the domestic manufacturing of MR instruments in China.

Commonly used molecular imaging methods include positron emission tomography (PET), hydrogen magnetic resonance spectroscopy (¹H MRS) and imaging (¹H MRSI), chemical exchange saturation transfer (CEST), and hyperpolarized ¹³C MRSI. As a cutting-edge molecular imaging method, deuterium metabolic spectroscopy (DMS) and imaging (DMI) has been recently developed and it distinguishes different tissues according to their specific glycometabolism. Compared with other molecular imaging methods, this promising technique has apparent advantages such as no radioactivity, good stability, and easy to maneuver. In this article, we review the progress of DMS/DMI and discuss its significance, future development, and potential clinical applications.

In recent years, the generative adversarial network (GAN) has attracted widespread attention with its unique adversarial training mechanism. Its applications have gradually extended to the field of medical imaging, and much excellent research has emerged. This paper reviews the research progress of the popular application for GAN in medical image translation. It starts with an introduction to the basic concepts of GAN and its typical variants, emphasizing on several GANs related to medical image translation. Then, the recent progress is summarized and analyzed from the perspectives of different target tasks and training modes. Finally, the remaining challenges of GAN in medical image translation and the directions of future development are discussed.