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: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。作为一种大有希望的解决办法,已经广泛调查了以深层次学习为基础的方法来减少磁共振成像的任务。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或需要新的购置设备,而且似乎比传统的人工制品消化方法效果更好。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。在这项工作中,我们提出了一个新的不受监督的腹部提取网络(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,实现了从人工领域向无运动区的过渡。不同于直接产生由运动腐蚀的MM成像的结果,我们采用了“腐蚀性提取”的战略,以便通过学习深层特征来间接纠正MR成像的影响。实验性结果显示我们的方法在现实的网络中可能处于高度状态。