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Abstract #4102

Novel and Efficient Generation of Diffeomorphic Motion Phantom

Xia Zhu1, Dipanjan Sengupta1, Theodore L Willke1, Andrew Beers2, and Jayashree Kalpathy-Cramer2,3

1Parallel Computing Lab, Intel Corporation, Hillsboro, OR, United States, 2Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 3The Center for Clinical Data Science, Massachusetts General Hospital and Brigham and Women’s Hospital, Boston, MA, United States

Dealing with motion artifacts is a fundamental preprocessing step in magnetic resonance imaging (MRI). In this work, we present three novel methods to generate MRI phantoms for diffeomorphic motion which can be used to validate motion correction algorithms. Previous research efforts for diffeomorphic motion generation, employ a brute force approach in which random pixel/voxel displacements are repeatedly generated until a topologically valid displacement is found. Such methods are not only time consuming but also doesn’t guarantee a valid displacement. Our approach algorithmically ensures that the topological properties are always maintained resulting in guaranteed diffeomorphic motion with much faster runtime.

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