Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
This work addresses a critical issue in medical imaging by enabling artifact correction without hard-to-acquire paired datasets, though it builds incrementally on previous research.
The paper tackles the problem of correcting rigid motion artifacts in brain MR images without requiring paired training data, achieving enhanced performance compared to existing supervised and unsupervised methods.
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.