Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the MRI gradient-echo phase signal and has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. The resulting susceptibility map is known to suffer from noise amplification and streaking artifacts. To address these challenges, we propose a model-based framework that permeates benefits from generative adversarial networks to train a regularization term that contains prior information to constrain the solution of the inverse problem, referred to as MoG-QSM. A residual network leveraging a mixture of least-squares (LS) GAN and the L1 cost was trained as the generator to learn the prior information in susceptibility maps. A multilayer convolutional neural network was jointly trained to discriminate the quality of output images. MoG-QSM generates highly accurate susceptibility maps from single orientation phase maps. Quantitative evaluation parameters were compared with recently developed deep learning QSM methods and the results showed MoG-QSM achieves the best performance. Furthermore, a higher intraclass correlation coefficient (ICC) was obtained from MoG-QSM maps of the traveling subjects, demonstrating its potential for future applications, such as large cohorts of multi-center studies. MoG-QSM is also helpful for reliable longitudinal measurement of susceptibility time courses, enabling more precise monitoring for metal ion accumulation in neurodegenerative disorders.

中文翻译：

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MoG-QSM：基于模型的定量对抗性深度学习网络

定量磁化率测绘（QSM）通过MRI梯度回波相位信号估算潜在的组织磁化率，并显示出在量化各种脑部疾病的组织磁化率方面的巨大潜力。但是，固有的不适定的逆问题将组织相位与潜在的磁化率分布相关联，影响了定量组织磁化率的准确性。已知所得的磁化率图遭受噪声放大和条纹伪影。为了应对这些挑战，我们提出了一个基于模型的框架，该框架可以渗透来自生成对抗网络的好处，以训练包含正则信息的正则化项，以约束反问题的解决方案，称为MoG-QSM。利用最小二乘（GAN）和L1成本的混合来训练残留网络作为生成器，以学习磁化率图中的先验信息。联合训练了多层卷积神经网络，以区别输出图像的质量。MoG-QSM从单方向相图生成高度准确的磁化率图。将定量评估参数与最近开发的深度学习QSM方法进行了比较，结果表明MoG-QSM达到了最佳性能。此外，从旅行对象的MoG-QSM图获得了更高的组内相关系数（ICC），表明了其在未来应用中的潜力，例如大型的多中心研究。MoG-QSM还有助于可靠地纵向测量磁化率时程，