In this study, we investigated how empathic neural responses unfold over time in different empathy networks when viewing same-race and other-race individuals in dynamic painful conditions. We recorded magnetoencephalography signals from Chinese adults when viewing video clips showing a dynamic painful (or non-painful) stimulation to Asian and White models' faces to trigger painful (or neutral) expressions. We found that perceived dynamic pain in Asian models modulated neural activities in the visual cortex at 100 ms-200 ms, in the orbitofrontal and subgenual anterior cingulate cortices at 150 ms-200 ms, in the anterior cingulate cortex around 250 ms-350 ms, and in the temporoparietal junction and middle temporal gyrus around 600 ms after video onset. Perceived dynamic pain in White models modulated activities in the visual, anterior cingulate, and primary sensory cortices after 500 ms. Our findings unraveled earlier dynamic activities in multiple neural circuits in response to same-race (vs other-race) individuals in dynamic painful situations.
Adult ; Humans ; Brain Mapping ; Pain ; Empathy ; Racism ; Gyrus Cinguli/physiology* ; Magnetic Resonance Imaging ; Brain/physiology*

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
Child ; Humans ; Cognitive Training ; Magnetic Resonance Imaging ; Brain ; Brain Mapping ; Motor Cortex

INTRODUCTION: A previous prospective, randomised controlled trial showed that animated videos shown to children before magnetic resonance imaging (MRI) scan reduced the proportion of children needing repeated MRI sequences and improved confidence of the children staying still for at least 30 min. Children preferred the interactive video. We hypothesised that the interactive video is non-inferior to showing two videos (regular and interactive) in improving children's cooperativeness during MRI scans. METHODS: In this Institutional Review Board-approved prospective, randomised, non-inferiority trial, 558 children aged 3-20 years scheduled for elective MRI scan from June 2017 to March 2019 were randomised into the interactive video only group and combined (regular and interactive) videos group. Children were shown the videos before their scan. Repeated MRI sequences, general anaesthesia (GA) requirement and improvement in confidence of staying still for at least 30 min were assessed. RESULTS: In the interactive video group ( n = 277), 86 (31.0%) children needed repeated MRI sequences, two (0.7%) needed GA and the proportion of children who had confidence in staying still for more than 30 min increased by 22.1% after the video. In the combined videos group ( n = 281), 102 (36.3%) children needed repeated MRI sequences, six (2.1%) needed GA and the proportion of children who had confidence in staying still for more than 30 min increased by 23.2% after the videos; the results were not significantly different between the two groups. CONCLUSION The interactive video group demonstrated non-inferiority to the combined videos group.
Child ; Humans ; Anesthesia, General ; Magnetic Resonance Imaging ; Prospective Studies ; Simulation Training ; Child, Preschool ; Adolescent ; Young Adult ; Video Recording

INTRODUCTION: Ultrasonography (US) is the current standard of care for imaging surveillance in patients at risk of hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and dual-sequence non-contrast-enhanced MRI (NCEMRI) for HCC surveillance in the local setting. METHODS: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed up for 40 years to estimate the patients' disease status, direct medical costs and effectiveness. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio were calculated. RESULTS: Exactly 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios - no surveillance, US surveillance and NCEMRI surveillance - were SGD 1,193/7.460 QALYs, SGD 8,099/11.195 QALYs and SGD 9,720/11.366 QALYs, respectively. CONCLUSION Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, contrast-enhanced MRI) based on patients' risk profiles.
Humans ; Adult ; Carcinoma, Hepatocellular/diagnostic imaging* ; Liver Neoplasms/diagnostic imaging* ; Cost-Effectiveness Analysis ; Cost-Benefit Analysis ; Quality-Adjusted Life Years ; Magnetic Resonance Imaging/methods*

Humans ; Lupus Vasculitis, Central Nervous System/pathology* ; Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods* ; Patients ; Lupus Erythematosus, Systemic/pathology* ; Brain/pathology*

Humans ; Myocarditis/diagnostic imaging* ; Magnetic Resonance Imaging ; Myocardium ; Giant Cells

Humans ; Myocarditis/diagnostic imaging* ; Magnetic Resonance Imaging ; Myocardium ; Giant Cells

When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
Algorithms ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.
Humans ; Imaging, Three-Dimensional/methods* ; Magnetic Resonance Imaging/methods* ; Ultrasonography/methods* ; Algorithms ; Surgery, Computer-Assisted/methods*

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q ^AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging/methods* ; Algorithms