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Abstract #1849

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation using a Reduced Model Joint Optimization

Melissa Haskell1,2, Stephen Cauley1,3, and Lawrence Wald1,3,4

1Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, 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

We approach the reconstruction of artifact-free images from an object undergoing unknown rigid-body transformations using a joint optimization of the final uncorrupted image and motion parameters. To characterize motion, the joint optimization must estimate 6 additional parameters for each shot in the image acquisition. We demonstrate an efficient method for reconstruction from translation-corrupted kspace data by examining iterative improvements to only a small, targeted subset of imaging voxels. The method can be enhanced by providing incomplete or noisy information from motion sensors or navigator measurements. We discuss generalizing our hybrid greedy and global step non-linear optimization to full rigid-body motion.

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