Keywords: Motion Correction, Motion CorrectionThis abstract presents a retrospective motion correction method for fMRI. The method alternates between motion estimation and motion-informed model-based reconstruction. Compared to registration-based correction, this approach resolves intra-frame, inter-shot motion without additional navigators. The open-source GPU-based implementation enables efficient correction/reconstruction for large-scale non-Cartesian fMRI data. With prospective experiments, we demonstrate that our approach outperformed retrospective registration by providing higher-resolution images with reduced false positives in activation maps.
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