QSMGAN：使用具有增加的感受场的3D生成对抗网络改进定量易感性映射
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
摘要：
定量磁敏感性成像（QSM）是一种功能强大的MRI技术，在许多神经系统疾病的组织易感性定量方面显示出巨大的潜力。然而，固有的不适定偶极子反演问题极大地影响了磁化率图的准确性。我们建议QSMGAN：基于3D U-Net架构的3D深度卷积神经网络方法，与输出相比，输入相位的感受野增加，并使用具有梯度惩罚训练策略的WGAN进一步细化网络。我们的方法产生准确的QSM地图从单一方向相位图有效地和执行显着优于传统的非学习为基础的偶极子反演算法。通过将该算法应用于一个看不见的病理-脑肿瘤患者辐射诱导的脑微出血，验证了该算法的泛化能力。
英文摘要：
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.
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