  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.
