Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies. Our codes are publicly available at https://github.com/YuSheng-Zhou/UNAEN.

翻译：运动伪影会降低磁共振成像（MRI）的质量，并对实现诊断结果和图像引导治疗构成挑战。近年来，有监督的深度学习方法已成为运动伪影抑制（MAR）的成功解决方案。这些方法的一个缺点在于它们依赖于获取成对的运动伪影损坏（MA-corrupted）和无运动伪影（MA-free）的MR图像用于训练目的。获取此类图像对较为困难，因此限制了有监督训练的应用。在本文中，我们提出了一种新颖的无监督异常提取网络（UNAEN）来缓解这一问题。我们的网络能够处理未配对的MA-corrupted和MA-free图像。它通过使用提出的伪影提取器从MA-corrupted图像中提取异常，将MA-corrupted图像转换为MA-reduced图像。该提取器显式地截取MA-corrupted MR图像中的残差伪影图，并利用重建器从MA-reduced图像中恢复原始输入。UNAEN的性能通过在各种公开可用的MRI数据集上进行实验，并与最先进的方法进行比较来评估。定量评估表明UNAEN优于其他MAR方法，并在视觉上显示出更少的残留伪影。我们的结果证实了UNAEN作为一种有前景的解决方案在实际临床环境中的应用潜力，具备提高诊断准确性和促进图像引导治疗的能力。我们的代码公开在https://github.com/YuSheng-Zhou/UNAEN。