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Abstract #1797

Multi-Tensor Filtering based on Expectation-Maximization Framework

Etienne St-Onge1,2, Benoit Scherrer2, Maxime Taquet2,3, and Simon Warfield2

1Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada, 2Computational Radiology, Harvard Medical School, Boston, MA, United States, 3ICTEAM, Université catholique de Louvain, Louvain-La-Neuve, Belgium

In this abstract, we introduce a new multi-tensor regularization and denoising technique based on Expectation-Maximization framework. To reduce filtering blurring effect and preserve sharp edges, we incorporated anisotropic regularization weight to the framework. We also utilize a tensor similarity metric, made from a quaternion representation, to improve the regularization and preserve tensor characteristics. Finally, we evaluate and compare filtering methods using a diffusion MRI synthetic phantom and in-vivo acquisition.

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