In this paper, we develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans.

翻译：在本文中,我们开发了一种高效的回溯式深层次学习方法,称为堆叠式的U-Net,具有自我辅助的前身,以解决磁力研究所的僵硬运动工艺品问题。拟议的工作利用了从腐蚀的图像本身中增加知识的前身,而不需要额外的对比数据。拟议中的网络通过分享同一扭曲主题的相毗相片的辅助信息来学习缺失的结构细节。我们进一步设计了一个精细的堆叠式U-Net,便于保存图像空间细节,从而改进像素到像素的依赖性。为了进行网络培训,模拟磁力运动工艺品是不可避免的。我们利用各种类型的图像前科进行密集分析:拟议的自我辅助的前身和前身,与同一主题的其他图像对比。实验性分析证明了我们自我辅助的前身的有效性和可行性,因为它不需要任何进一步的数据扫描。