Integrating Deep Learning for Detection and Correction of Motion Artifacts in multi-echo GRE MRI

Motivation: Motion artifacts in mGRE MRI scans reduce image quality, increasing the risk of misdiagnosis and often necessitating repeat scans, negatively impacting patient care and diagnostic accuracy. Goal(s): Our goal is to develop a novel deep learning-based framework for reducing motion artifacts in mGRE MRI k-space data, ensuring the generation of high-quality images. Approach: The methodology proceeds by detecting and correcting motion-corrupted phase encoding lines within the k-space domain, employing a two-stage DeepFillv2 algorithm. It also integrates motion parameter estimation to enhance the framework's robustness. Results: The model’s effectiveness in identifying and rectifying motion artifacts in MRI was confirmed through quantitative and qualitative evaluation. Impact: The proposed k-space domain framework progresses by identifying phase encoding lines affected by motion and repairing them using deep learning techniques, thereby proving improved image quality and demonstrating potential as a diagnostic aid.