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Abstract #0499

Hybrid Prospective & Retrospective Head Motion Correction System to Mitigate Cross-Calibration Errors

Murat Aksoy1, Christoph Forman1,2, Matus Straka1, Tolga ukur3, Samantha Jane Holdsworth1, Stefan Tor Skare1,4, Juan Manuel Santos3, Joachim Hornegger2, Roland Bammer1

1Department of Radiology, Stanford University, Stanford, CA, United States; 2Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany; 3Electrical Engineering, Stanford University, Stanford, CA, United States; 4Karolinska Institute, Stockholm, Sweden

Correction of motion artifacts in MRI is essential to assure diagnostic image quality. In case where external pose information is used for motion-correction, cross-calibration errors may impair image quality. In this study, we propose a combined prospective & retrospective approach to prospectively correct for motion and to mitigate residual image distortions which emanate from subtle cross-calibration errors. Specifically, a single camera mounted on the head coil was used to measure and correct patient motion in real-time. Resulting data inconsistencies emanating primarily from cross-calibration errors were removed by a retrospective autofocusing algorithm wherein k-space was divided into segments. The relative rotation and translation needed to realign these segments were determined by means of entropy-based autofocusing. Phantom and in-vivo results show that in the presence of inaccuracies in cross-calibration, the current method provides improved image quality over prospective motion correction only.