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.