计算机工程与应用2024，Vol.60Issue(13)：228-236,9.DOI:10.3778/j.issn.1002-8331.2304-0099
融合注意力和空洞编码解码的3D-MRI超分辨率算法
3D-MRI Super-Resolution Algorithm Fusing Attention and Dilated Encoder-Decoder
摘要
Abstract
In order to improve the spatial resolution of brain magnetic resonance imaging(MRI)images,the super resolu-tion(SR)reconstruction method based on deep convolutional neural network(CNN)has achieved remarkable results.Increasing network depth or width usually can expand the receptive field of the network to improve the quality of recon-struction,but it is difficult to train the network due to the large parameter quantity and huge computing requirements.Meanwhile,the network does not pay enough attention to the medium and high frequency features of the image,which affects the quality of reconstruction.To solve the above problems,this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction.Firstly,feature extraction is carried out in the low reso-lution space,and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts.Secondly,a serial connected channel-space attention module is constructed to cap-ture the interdependence between the feature channels by channel attention,and the spatial attention is increased to assign weight to information of different positions,effectively enhancing the network's learning of medium and high frequency information.Finally,subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption.Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio(PSNR),structural similarity(SSIM)and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms.关键词
超分辨率重建/注意力机制/亚像素卷积/空洞卷积/3D-MRIKey words
super-resolution reconstruction/attention mechanism/subpixel convolution/dilated convolution/3D-MRI分类
信息技术与安全科学引用本文复制引用
张金迪,贾媛媛,祝华正,李洪碧,杜井龙..融合注意力和空洞编码解码的3D-MRI超分辨率算法[J].计算机工程与应用,2024,60(13):228-236,9.基金项目
重庆市自然科学基金面上项目(CSTB2023NSCQ-MSX0130) （CSTB2023NSCQ-MSX0130）
重庆市自然科学基金(cstc2021jcyj-bshX0168) （cstc2021jcyj-bshX0168）
重庆市教委青年研究项目(KJQN202101501,KJQN202001513) （KJQN202101501,KJQN202001513）
重庆医科大学研究生智慧医学专项(YJSZHYX202218). （YJSZHYX202218）
