Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Deep Learning

Background:

• Motion artifacts significantly degrade Magnetic Resonance Imaging (MRI) quality, hindering diagnosis and image-guided therapies.
• Supervised deep learning methods for motion artifact reduction (MAR) require paired corrupted and artifact-free images, which are difficult to obtain.
• This limitation restricts the practical application of supervised MAR techniques in clinical settings.

Purpose of the Study:

• To propose a novel unsupervised deep learning network, UNAEN, for motion artifact reduction in MRI.
• To enable MAR using unpaired corrupted and artifact-free MR images, overcoming the limitations of supervised methods.
• To enhance the quality of MRI scans for improved diagnostic accuracy and image-guided therapies.

Main Methods:

• Developed a UNsupervised Abnormality Extraction Network (UNAEN) that operates on unpaired MRI datasets.
• Implemented an artifact extractor to identify and isolate artifact maps from corrupted MR images.
• Utilized a reconstructor to restore image quality from the artifact-reduced images.

Main Results:

• UNAEN demonstrated superior performance compared to state-of-the-art MAR methods on various public MRI datasets.
• Quantitative evaluations confirmed the effectiveness of UNAEN in reducing motion artifacts.
• Visual assessments showed significantly fewer residual artifacts in images processed by UNAEN.

Conclusions:

• UNAEN offers a promising unsupervised solution for motion artifact reduction in MRI.
• The network's ability to work with unpaired data makes it suitable for real-world clinical applications.
• UNAEN has the potential to enhance diagnostic accuracy and facilitate advanced image-guided therapies.