Unsupervised Adversarial Correction of Rigid MR Motion Artifacts

Abstract

Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. However, these approaches suffer from the limitation of required paired co-registered datasets for training which are often hard or impossible to acquire. Building upon our previous work, we introduce a new adversarial framework with a new generator architecture and loss function for the unsupervised correction of severe rigid motion artifacts in the brain region. Quantitative and qualitative comparisons with other supervised and unsupervised translation approaches showcase the enhanced performance of the introduced framework.

arXiv commentSubmitted to IEEE ISBI 2020

References in corpus (4)

• Spectral Normalization for Generative Adversarial Networks
• Self-Attention Generative Adversarial Networks
• Unsupervised Medical Image Translation Using Cycle-MedGAN
• Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Cited by in corpus (5)

• Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
• Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
• Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
• Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning
• Deep learning-based bias transfer for overcoming laboratory differences of microscopic images