Research of MRI Reconstruction Method by Using De-aliasing Wasserstein Generative Adversarial Networks with Gradient Penalty

被引：0

作者：

Yuan Z.-H. ^{[1
]}

Jiang M.-F. ^{[1
]}

Li Y. ^{[1
]}

Zhi M.-H. ^{[1
]}

Zhu Z.-J. ^{[2
]}

机构：

[1] School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou

[2] Department of Cardiology, The 117th Hospital of PLA, Hangzhou

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 10期

关键词：

Image reconstruction; Magnetic resonance imaging (MRI); Perceptual loss; WGAN-GP;

D O I：

10.3969/j.issn.0372-2112.2020.10.002

中图分类号：

学科分类号：

摘要：

In this paper,we propose an improved Wasserstein generative adversarial network (WGAN),de-aliasing Wasserstein generative adversarial network with Gradient Penalty (DAWGAN-GP),for magnetic resonance imaging (MRI) reconstruction.This method uses WGAN to replace the traditional GAN,and combined the gradient penalty to improve the training speed and to solve the slow convergence problem of WGAN.In addition,for better preservation of the fine structures in the reconstructed images,we incorporate perceptual loss,pixel loss and frequency loss into the loss function for training the network.Compared with other state-of-the-art deep learning methods for MR images reconstruction,DAWGAN-GP method outperforms all other methods and can provide superior reconstruction with improved peak signal to noise ratio (PSNR) and better structural similarity index measure (SSIM). © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：1883 / 1890

页数：7

共 21 条

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