Estimating intrascan subject motion enables the reduction of motion artifacts but often requires further calibration data e.g. from an additional motion-free reference. In this work, we explore how the reuse of supplementary scans in the imaging workflow can be used as motion calibration data. More specifically, the preceding parallel imaging calibration scan is reutilized to support a Deep Learning (DL) approach for estimating motion. Results are presented which indicate that DL, in contrast to a conventional optimization approach, can extract the motion and improve the image quality despite contrast differences between the calibration and imaging scans.
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