Keywords: Motion Correction, Motion CorrectionA motion correction framework was developed to suppress artifacts from rigid and non-rigid motion using a combination of model-based and ML-based approaches. The performance of this framework was compared with model-based only and ML-based only motion correction approaches on motion-simulated data and motion-corrupted in-vivo data using visual inspection and image quality metrics. The combined approach showed superior image quality than model-based and ML-based approaches applied individually.
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