Zhenyu Zhou1, 2, Yijun Liu3, Guang Cao1, Karen M. von Deneen3, Dongrong Xu2
1Global Applied Science Laboratory, GE Healthcare, Beijing, China; 2MRI Unit, Columbia University, New York, NY, United States; 3McKnight Brain Institute, University of Florida, Gainesville, FL, United States
Diffusion weighted imaging is always influenced by both thermal noise and spatially and/or temporally varying artifacts such as subject motion and cardiac pulsation. Motion artifacts are particularly prevalent, especially when scanning an uncooperative population with several disorders. In this study, we proposed a classifier work frame which can classify DWIs as normal images or motion artifacts. It achieves better performance in tensor estimation by automatic unvoxel-wise outlier rejection compared with manual and visual inspection, and previous voxel-wise outlier rejection methods. The proposed method could potentially remove the influence of unexpected motion artifacts in DWI acquisitions.
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