融合注意力机制的多尺度残差Unet的磁共振图像重建  

Magnetic Resonance Image Reconstruction of Multi-scale Residual Unet Fused with Attention Mechanism

作　　者：李奕洁 杨馨雨 杨晓梅[1] Li Yijie;YANG Xinyu;YANG Xiaomei(College of Electrical Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电气工程学院,四川成都610065

出　　处：《波谱学杂志》2023年第3期307-319,共13页Chinese Journal of Magnetic Resonance

摘　　要：为了提高磁共振图像在欠采样下重建的质量,本文融合注意力机制和多尺度残差卷积构建Unet网络,实现磁共振图像在欠采样下的重建算法.为增强网络特征的表现能力,以及防止网络训练中梯度消失与退化的问题,在Unet网络的编码路径中引入多尺度残差卷积,提取不同尺度的特征信息;为能准确地恢复图像的细节纹理特征,在Unet网络编码和解码路径的跳层拼接部分引入卷积注意力块,对细节纹理等关键信息进行不同程度的响应.实验表明,本文方法可通过欠采样k-空间数据快速重建出细节纹理清晰且无重叠伪影的高质量磁共振图像.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.

关 键 词：磁共振成像 图像重建 Unet 注意力机制 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]