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Abstract #1305

Retrospective motion correction of head rotations in 2D RARE brain images using TArgeted Motion Estimation and Reduction (TAMER)

Melissa W. Haskell1,2, Stephen F. Cauley1,3, and Lawrence L. Wald1,3,4

1A. A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Charlestown, MA, United States, 2Graduate Program in Biophysics, Harvard University, Cambridge, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States

RARE/TSE/FSE imaging is the most common brain sequence, but can be severely degraded by patient motion. While 2D navigated versions (PROPELLER) and motion-tracking approaches exist, they are not widely used. We introduced a data-consistency based retrospective method, TAMER, whereby the image and motion parameters are jointly estimated by minimizing data consistency error of a SENSE+motion forward model. We employ reduced modeling techniques which assess only a few targeted voxels at each step to make the large non-linear estimation problem computationally achievable. We demonstrate the approach to mitigating rotations in phantom and human scans in addition to previously reported translation mitigation.

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