Cai Xin, Hou Xuewen, Yang Guang, Nie Shengdong
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Jun 25;40(3):582-588. doi: 10.7507/1001-5515.202204007.
Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
磁共振成像(MRI)是一种重要的医学成像方法,其主要局限性在于成像机制导致扫描时间长,增加了患者的费用和检查等待时间。目前,并行成像(PI)和压缩感知(CS)以及其他重建技术已被提出用于加速图像采集。然而,PI和CS的图像质量取决于图像重建算法,在图像质量和重建速度方面都远不能令人满意。近年来,基于生成对抗网络(GAN)的图像重建因其出色的性能而成为磁共振成像领域的研究热点。在本综述中,我们总结了GAN在MRI重建中单模态和多模态加速应用的最新进展,希望为感兴趣的研究人员提供有用的参考。此外,我们分析了现有技术的特点和局限性,并预测了该领域的一些发展趋势。