典型文献
Motion artifact correction for MR images based on convolutional neural network
文献摘要：
Magnetic resonance imaging(MRI)is a common way to diagnose related diseases.However,the magnetic resonance(MR)images are easily defected by motion artifacts in their acquisition process,which affects the clinicians'diagnosis.In order to correct the motion artifacts of MR images,we propose a convolutional neural network(CNN)-based method to solve the problem.Our method achieves a mean peak signal-to-noise ratio(PSNR)of(35.212±3.321)dB and a mean structural similarity(SSIM)of 0.974±0.015 on the test set,which are better than those of the comparison methods.
文献关键词：
中图分类号：
作者姓名：
ZHAO Bin;LIU Zhiyang;DING Shuxue;LIU Guohua;CAO Chen;WU Hong
作者机构：
College of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,China;Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Nankai University,Tianjin 300350,China;School of Artificial Intelligence,Guilin University of Electronic Technology,Guilin 541004,China;Department of Medical Imaging,Tianjin Huanhu Hospital,Tianjin 300350,China
文献出处：
引用格式：
[1]ZHAO Bin;LIU Zhiyang;DING Shuxue;LIU Guohua;CAO Chen;WU Hong-.Motion artifact correction for MR images based on convolutional neural network)[J].光电子快报（英文版）,2022(01):54-58
A类：
B类：
Motion,correction,images,convolutional,neural,network,Magnetic,resonance,imaging,common,way,diagnose,related,diseases,However,magnetic,are,easily,defected,by,motion,artifacts,their,acquisition,process,which,affects,clinicians,diagnosis,In,order,propose,solve,problem,Our,achieves,mean,peak,signal,noise,ratio,PSNR,dB,structural,similarity,SSIM,test,set,better,than,those,comparison,methods
AB值：
0.627737
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