Motion artifact reduction is one of the most concerned problems in magnetic resonance imaging. As a promising solution, deep learning-based methods have been widely investigated for artifact reduction tasks in MRI. As a retrospective processing method, neural network does not cost additional acquisition time or require new acquisition equipment, and seems to work better than traditional artifact reduction methods. In the previous study, training such models require the paired motion-corrupted and motion-free MR images. However, it is extremely tough or even impossible to obtain these images in reality because patients have difficulty in maintaining the same state during two image acquisition, which makes the training in a supervised manner impractical. In this work, we proposed a new unsupervised abnomality extraction network (UNAEN) to alleviate this problem. Our network realizes the transition from artifact domain to motion-free domain by processing the abnormal information introduced by artifact in unpaired MR images. Different from directly generating artifact reduction results from motion-corrupted MR images, we adopted the strategy of abnomality extraction to indirectly correct the impact of artifact in MR images by learning the deep features. Experimental results show that our method is superior to state-of-the-art networks and can potentially be applied in real clinical settings.

翻译：运动伪影抑制是磁共振成像领域最受关注的问题之一。作为有前景的解决方案，基于深度学习的方法已被广泛研究用于MRI伪影抑制任务。作为一种回顾性处理方法，神经网络无需增加额外采集时间或新采集设备，且效果优于传统伪影抑制方法。既往研究表明，此类模型的训练需要成对的运动伪影图像与无伪影MR图像。然而在实际临床中，由于患者两次图像采集期间难以维持相同状态，获取此类配对图像极为困难甚至不可能，这使得有监督训练方式缺乏可行性。本研究提出一种新型无监督异常提取网络（UNAEN）以缓解该问题。该网络通过处理非配对MR图像中由伪影引入的异常信息，实现从伪影域到无伪影域的转换。与直接从运动伪影MR图像生成伪影抑制结果不同，我们采用异常提取策略，通过学习深层特征间接修正MR图像中的伪影影响。实验结果表明，本方法优于现有最优网络，并具有在真实临床环境中应用的潜力。