Motion artifact correction for MR images based on convolutional neural network

被引：0

作者：

Bin Zhao

Zhiyang Liu

Shuxue Ding

Guohua Liu

Chen Cao

Hong Wu

机构：

[1] Nankai University,College of Electronic Information and Optical Engineering

[2] Nankai University,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology

[3] Guilin University of Electronic Technology,School of Artificial Intelligence

[4] Tianjin Huanhu Hospital,Department of Medical Imaging

来源：

Optoelectronics Letters | 2022年 / 18卷

关键词：

D O I：

暂无

中图分类号：

学科分类号：

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.

引用

页码：54 / 58

页数：4

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