QG-MoCo: Quality-Guided Coarse- and Fine-Grained Path Selection For MRI Motion Correction

Motivation: Structural MR images often suffer from motion artifacts, degrading image quality and hindering subsequent image anlaysis. Goal(s): We propose a quality-guided motion correction (QG-MoCo) procedure to efficiently reduce motion artifacts in structural MR images by deep learning. Approach: We develop QG-MoCo for operating the MoCo process in a novel and efficient strategy. The overall framework consists of two MoCo networks for basic 3D motion correction and residual texture enhancement separately, with the coarse- and fine-grained path guidance for optimal correction effect and computation cost. Results: QG-MoCo demonstrates high efficacy in motion feature extraction and reduction, presenting a new quality-aware MoCo procedure. Impact: The quality-guided MoCo method effectively reduces 3D motion artifacts following the automatic selective path in different granularity for progressive correction. Emphasizes the benefits on model accuracy and efficiency by taking MoCo operations based on the routing strategy with quality consideration.