Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2301.01732
