基于语义对比生成对抗网络的高倍欠采MRI重建

利用数据的稀疏性从随机欠采样的K空间重建图像,是解决磁共振成像(Magnetic Resonance Imaging,MRI)因采集时间过长而应用受限问题的主要手段。然而,现有的方法重建高倍欠采图像时纹理细节丢失严重。针对这一问题,借鉴生成对抗网络的对抗学习思想,文中提出一种基于语义对比生成对抗网络的高倍欠采MRI重建方法(Semantic-Contrast Generative Adversarial Network,SC-GAN)。该方法由连续的两部分组成。第一部分将笛卡尔高倍随机欠采样MRI图像输入基于U-NET的生成器,与鉴别器不断博弈对抗生成初步重建图像,以此构建重建子网;另一部分是语义对比子网,通过VGG-16比较初步重建图像与全采样图像的语义信息,比较结果反馈给第一部分进行参数调优,直到生成最佳的重建图像。实验结果表明,在加速因子高达7(14%)时,获得了主客观评价结果均较好的重建图像。与先进的重建方法相比,所提方法的内存损耗更低、收敛速度更快且纹理细节更丰富,可为下一代MRI机器的研发提供算法支持。

Semantic-contrast Generative Adversarial Network Based Highly Undersampled MRI Reconstruction

Exploiting the sparse nature of data to reconstruct image from randomly undersampled K-space is the main method to solve the problem of limited application of magnetic resonance imaging(MRI)due to long acquisition time.However,the loss of textures is serious when the existing methods are used to reconstruct the highly undersampled MRI.Aiming at this problem,referring to the adversarial learning idea of generative adversarial networks(GAN),a novel method for highly undersampled MRI reconstruction based on semantic-contrast generative adversarial network(SC-GAN)is proposed.This method consists of two successive parts.In the first part,the MRI image with Cartesian highly random undersampled is input into the U-NET-based ge-nerator to compete with the discriminator for generating the initial reconstructed image,so as to construct the reconstruction subnet.The other part is the semantic contrast subnet,which compares the semantic information between the initial reconstructed image and its fully-sampled image with VGG-16 network.Comparison results are fed back to the first part for parameter adjustment until the best reconstructed image is generated.Experimental results show that the excellent reconstructed results are verified by subjective and objective evaluation when the acceleration factor is up to 7(14%).Compared with the state-of-art methods,the proposed SC-GAN has lower memory consumption,faster convergence speed and more textures,and can provide algorithm support for the development of next-generation MRI machines.