Motion artifact reduction is one of the most concerned problems in magnetic resonance imaging. In recent years, 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 paper, we proposed a new unsupervised abnormality 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 abnormality 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:在磁共振成像中,减少人工制品是磁共振成像中最令人关切的问题之一。近年来,在磁共振成像中,对深层次的学习方法进行了广泛调查,对减少人工制品任务进行了广泛调查。作为一种回顾性处理方法,神经网络不花费额外的购置时间,也不需要新的购置设备,而且似乎比传统的人工制品减少方法效果更好。在前一次研究中,培训这些模型需要配对的运动碎裂和无运动的MR图像。然而,在现实中获取这些图像是非常困难的,甚至甚至是不可能的,因为病人在获得两张图像的过程中很难维持同样的状态,这使得以监督的方式进行培训变得不切实际。在本文中,我们提议建立一个新的不受监督的异常提取网络(UNAEN)来缓解这一问题。我们的网络通过处理未受干扰的MRM图像中人工制品带来的异常信息,实现了从工艺品向无运动的无动场域的转变。不同于直接产生由运动碎动的MR图像产生的工艺减少结果,我们采用了异常提取战略,通过了解深度特征来间接纠正手工艺品在MR图像中的影响。实验结果显示,我们的方法在现实的网络中可能被应用到状态。