Multimodal medical images can be used in a multifaceted approach to resolve a wide range of medical diagnostic problems. However, these images are generally difficult to obtain due to various limitations, such as cost of capture and patient safety. Medical image synthesis is used in various tasks to obtain better results. Recently, various studies have attempted to use generative adversarial networks for missing modality image synthesis, making good progress. In this study, we propose a generator based on a combination of transformer network and a convolutional neural network (CNN). The proposed method can combine the advantages of transformers and CNNs to promote a better detail effect. The network is designed for positron emission tomography (PET) to computer tomography synthesis, which can be used for PET attenuation correction. We also experimented on two datasets for magnetic resonance T1- to T2-weighted image synthesis. Based on qualitative and quantitative analyses, our proposed method outperforms the existing methods.

Objective. Given the different noise distribution information of global and local magnetic resonance (MR) images, this study aims to extend the current work on convolutional neural networks that preserve global structure and local details in MR image denoising tasks. Approach. This study proposed a parallel and serial network for denoising 3D MR images, called 3D-PSNet. We use the residual depthwise separable convolution block to learn the local information of the feature map, reduce the network parameters, and thus improve the training speed and parameter efficiency. In addition, we consider the feature extraction of the global image and utilize residual dilated convolution to process the feature map to expand the receptive field of the network and avoid the loss of global information. Finally, we combine both of them to form a parallel network. What’s more, we integrate reinforced residual convolution blocks with dense connections to form serial network branches, which can remove redundant information and refine features to further obtain accurate noise information. Main results. The peak signal-to-noise ratio, structural similarity index measure, and root mean square error metrics of 3D-PSNet are as high as 47.79%, 99.81%, and 0.40%, respectively, achieving competitive denoising effect on three public datasets. The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets. Significance. The proposed 3D-PSNet takes advantage of multi-scale receptive fields, local feature extraction and residual dense connections to more effectively restore the global structure and local fine features in MR images, and is expected to help doctors quickly and accurately diagnose patients’ conditions.

Brain magnetic resonance image registration is a fundamental task in medical image analysis. However, achieving fine-grained alignment remains challenging, particularly in brain regions with small volumes or complex anatomical structures. Generative adversarial networks (GANs) have provided new opportunities to improve registration accuracy through adversarial learning. In this study, we propose MATGAN, a novel GAN-based registration framework that integrates a multi-scale attention mechanism and Huber loss into the adversarial learning process to achieve more robust and accurate image alignment. Specifically, a multi-scale attention module is embedded into the discriminator to extract semantic representations at different resolutions, thereby enhancing its ability to distinguish real deformations from generated ones. The similarity feedback provided by the discriminator is further transferred to the registration network, guiding it to generate deformation fields with improved anatomical consistency. In addition, Huber loss is incorporated into the similarity constraint to reduce the influence of outliers during optimization and improve the robustness and stability of network training. Through the adversarial interaction between the registration network and the discriminator, MATGAN enables more precise deformation estimation and better structural alignment. Experimental results on three public brain MRI datasets demonstrate that MATGAN achieves effective registration performance.

Arrhythmia is a group of common diseases related to irregular heart rate. Accurate classification of electrocardiogram is very important for college students to detect heart disease. Experts will spend a major expenditure of time and effort in the examination and analysis of college students' ECG waveform. In order to solve this problem, this paper proposes a method of college students' arrhythmia detection based on deep learning, which classifies different types of arrhythmias. Firstly, 1-D ECG signals are converted into 2-D ECG images. Then the signals of different channels are fused and finally input into the network. In this work, we do experiments on the basis of residual network. For verifying the feasibility of this method, it is trained and verified in the public data set MIT-BIH, and the classification accuracy reaches 99.00%. The experimental results proved that it can effectively and reliably identify ECG signals, and has potential clinical application value. It can assist doctors to screen college students for arrhythmias.