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Abstract #1417

DTI Smoothing by Hierarchical, Adaptive and Robust Strategy

Songyuan Tang1, Yong Fan1, Hongtu Zhu2, Wei Gao1, Weili Lin1, Dinggang Shen1

1Department of Radiology and BRIC,University of North Carolina, Chapel Hill, NC, USA; 2Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA

We have proposed a new method for adaptively estimating and smoothing DTs based on DWIs. The proposed method has three distinct novelties. First, it used robust anisotropic diffusion in log-Euclidean space for DT smoothing. Second, it employed a hierarchical strategy to adaptively smooth DTs. Finally, to achieve adaptive tensor estimation, an iterative estimation of weighting coefficients was employed to characterize the similarity between neighboring tensors. Both the results on simulated and human data suggest that the proposed approach outperforms the currently existing approach. Evaluation of the performance of the proposed method on large dataset is currently ongoing.