PET and MRI image fusion based on a dense convolutional network with dual attention.

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第一作者： Bicao,Li

第一单位： School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China; School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, 450000, China. Electronic address: lbc@zut.edu.cn.

作者： Bicao,Li ^[1] ; Jenq-Neng,Hwang ^[2] ; Zhoufeng,Liu ^[3] ; Chunlei,Li ^[4] ; Zongmin,Wang ^[5]

作者单位： School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China; School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, 450000, China. Electronic address: lbc@zut.edu.cn. ^[1] Department of Electrical Engineering, University of Washington, Seattle, WA, 98195, USA. Electronic address: hwang@uw.edu. ^[2] School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China. Electronic address: liuzhoufeng@zut.edu.cn. ^[3] School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China. Electronic address: lichunlei1979@sina.com. ^[4] School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, 450000, China. Electronic address: zmwang@ha.edu.cn. ^[5]

关键词 Channel attention Densely connected network Image fusion PET and MRI images Spatial attention

主题词正电子发射断层显像术(Positron-Emission Tomography);磁共振成像(Magnetic Resonance Imaging);注意力(Attention);图像处理, 计算机辅助(Image Processing, Computer-Assisted)

DOI 10.1016/j.compbiomed.2022.106339

PMID 36459810

发布时间 2022-12-25

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