Retrospective Motion Artifact Correction Using Refinement U-Nets with Wavelet Affine Transformations and Adaptive Multi-Loss Normalization

Motivation: MRI scans have inherently lengthy acquisition times, making them susceptible to motion artifacts that can degrade AI performance and compromise clinical diagnoses accuracy. Goal(s): Our goal was to incorporate wavelet transformations and adaptive multi-loss functions to optimize artifact correction and achieve more accurate, high-quality MRI images Approach: Our method integrates wavelet transformations within a refinement U-Net architecture, combined with adaptive multi-loss normalization, to accurately address artifact correction using real motion patterns from motion-free MRI scans. Results: Our method achieved significant improvements, raising SSIM from 76.85% to 92.92% and PSNR from 24.96 to 32.78, demonstrating effective artifact correction and surpassing other retrospective methods. Impact: Correcting motion artifacts in MRI scans enhances image quality, making them more reliable for clinical diagnosis. Additionally, using this approach as a preprocessing step for tasks like registration and segmentation boosts model accuracy and supports improved diagnostic outcomes.