南方医科大学学报

南方医科大学学报 ›› 2023, Vol. 43 ›› Issue (8): 1402-1409.doi: 10.12122/j.issn.1673-4254.2023.08.18

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基于物理模型的级联生成对抗网络加速定量多参数磁共振成像

刘羽轩，楚智钦，张 煜   

  1. 南方医科大学生物医学工程学院//广东省医学图像处理重点实验室，广东 广州 510515
  • 出版日期:2023-08-20 发布日期:2023-09-13

Physical model-based cascaded generative adversarial networks for accelerating quantitative multi-parametric magnetic resonance imaging

LIU Yuxuan, CHU Zhiqin, ZHANG Yu   

  1. School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China
  • Online:2023-08-20 Published:2023-09-13

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摘要： 目的 探讨基于物理模型的级联生成对抗网络使用原始的多回波多线圈k空间数据加速定量多回波多参数磁共振成像方法的可行性分析与解释。方法 提出了一种基于物理模型的级联生成对抗网络，利用多域信息联合训练以及通过系统矩阵学习图像重建所需的关键参数，并自适应地优化k空间生成器和图像生成器结构来增强图像特征信息以获得高质量的重建图像。使用原始的多回波多线圈k数据加速多对比度多参数磁共振图像成像。提出了基于物理驱动的深度学习重建方法，通过建立系统矩阵函数而不是直接通过模型端到端训练的方式来增加模型的泛化能力和提高模型性能。结果 在整体回波图像质量评价方面，该模型在80例测试集上的重建图像的平均PSNR值为34.13，SSIM为0.965，NRMSE为0.114，大幅度优于本文的其它对比方法。在多对比度多参数图像重建方面，该模型评估的PDW、T1W以及T2* Map的PSNR分别为38.87、35.62和34.38，在定量上也显著优于其它对比方法，并拟合出更为清晰的大脑灰质、白质和脑脊液特征。除此以外，在重建时间相差不到10%的前提下与现有的方法相比，本研究的方法对PSNR、SSIM和NRMSE的指标提升最高可达到20%。结论 相比现有的方法，基于物理模型的级联生成对抗网络方法可以重建出更多的图像细节和特征，从而提高了图像的质量和准确性，并有望将其应用于临床诊疗流程中。

关键词: 加速磁共振成像, 多对比度多参数, 物理模型, 级联生成对抗网络, 多域联合学习

Abstract: Objective To explore the feasibility and interpretation of physical model- based cascaded generative adversarial networks for accelerating quantitative multi-echo multi-parametric magnetic resonance imaging using raw multi-echo multi-coil k-space data. Methods A physical model- based cascaded generative adversarial network is proposed to enhance image feature information to obtain high-quality reconstructed images using joint training of multi-domain information and learning of key parameters required for image reconstruction through a system matrix and adaptively optimizing the k-space generator and image generator structures. Raw multi-echo multi-coil k-space data are used to accelerate multi-contrast multi-parametric magnetic resonance imaging. A physically driven deep learning reconstruction method is used to increase the generalization capability and improve the model performance by building a system matrix function instead of direct end-to-end training of the model. Results In terms of overall image quality, the proposed model achieved significant improvements compared to other methods. On an 80- case test set, the average PSNR value of the reconstructed images was 34.13, SSIM was 0.965, and NRMSE was 0.114. In terms of multi-contrast multi-parametric image reconstruction, the model achieved PSNR values of 38.87 for PDW, 35.62 for T1W, and 34.38 for T2* Map, which were significantly better than those of other methods for quantitative evaluation. The model also produced clearer features of the brain gray matter, white matter, and cerebrospinal fluid. Furthermore, compared with the existing methods with a reconstruction time difference of less than 10% , the proposed method achieved the highest improvement of up to 20% in the metrics of PSNR, SSIM, and NRMSE. Conclusion Compared with other existing methods, the physical model-based cascaded generative adversarial networks can reconstruct more image details and features, thus improving the quality and accuracy of the reconstructed images.

Key words: accelerated magnetic resonance imaging, multi-contrast and multi-parameter, physical model, cascaded generative adversarial networks, multi-domain joint learning