Keywords: Artifacts, Motion Correction
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.
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