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Abstract #3558

Making the Robust Tensor Estimation Approach: "RESTORE" More Robust

Lin-Ching Chang1, Lindsay Walker2, Carlo Pierpaoli2

1Department of Electrical Engineering and Computer Science , The Catholic University of America, Washington, DC , USA; 2National Institutes of Health, Bethesda, MD, USA

The Robust Estimation of Tensors by Outlier Rejection (RESTORE) has been demonstrated to be an effective method for improving tensor estimation on a voxel by voxel basis in the presence of artifactual data points in the diffusion weighted images. Despite the very good performance of the RESTORE algorithm, there are some limitations and opportunities for improvement. This paper extends our previous work of improving diffusion tensor estimation by proposing two practical constraints in the outlier rejection process that make the RESTORE method more robust.