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 on 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.

翻译：运动伪影会损害磁共振成像（MRI）质量，并对实现诊断结果和图像引导治疗构成挑战。近年来，监督式深度学习方法已成为运动伪影减少（MAR）的成功解决方案。这些方法的一个缺点是依赖获取配对的一组运动伪影污染（MA-污染）和无运动伪影（MA-自由）的MR图像用于训练。获取此类图像对非常困难，这限制了监督训练的应用。本文提出了一种新颖的无监督异常提取网络（UNAEN）以缓解这一问题。我们的网络能够处理未配对的MA-污染和MA-自由图像。通过使用所提出的伪影提取器从MA-污染图像中显式截取残差伪影图，以及一个重建器从MA-减少图像中恢复原始输入，该网络将MA-污染图像转换为MA-减少图像。通过在多种公开MRI数据集上进行实验，并与现有最优方法进行比较，评估了UNAEN的性能。定量评估表明UNAEN优于其他MAR方法，且视觉上残留伪影更少。我们的结果证实了UNAEN作为实际临床环境中可行解决方案的潜力，具有提高诊断准确性和促进图像引导治疗的能力。