Skip to Main content Skip to Navigation
New interface
Journal articles

A Robust Variational Approach for Simultaneous Smoothing and Estimation of DTI

Abstract : Estimating diffusion tensors is an essential step in many applications -- such as diffusion tensor image (DTI) registration, segmentation and fiber tractography. Most of the methods proposed in the literature for this task are not simultaneously statistically robust and feature preserving techniques. In this paper, we propose a novel and robust variational framework for simultaneous smoothing and estimation of diffusion tensors from diffusion MRI. Our variational principle makes use of a recently introduced total Kullback-Leibler (tKL) divergence for DTI regularization. tKL is a statistically robust dissimilarity measure for diffusion tensors, and regularization by using tKL ensures the symmetric positive definiteness of tensors automatically. Further, the regularization is weighted by a non-local factor adapted from the conventional non-local means filters. Finally, for the data fidelity, we use the nonlinear least-squares term derived from the Stejskal-Tanner model. We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples.
Document type :
Journal articles
Complete list of metadata
Contributor : Rachid Deriche Connect in order to contact the contributor
Submitted on : Thursday, April 4, 2013 - 5:08:03 PM
Last modification on : Saturday, November 19, 2022 - 3:58:53 AM

Links full text



Meizhu Liu, Baba Vemuri, Rachid Deriche. A Robust Variational Approach for Simultaneous Smoothing and Estimation of DTI. NeuroImage, 2013, 67, pp.33-41. ⟨10.1016/j.neuroimage.2012.11.012⟩. ⟨hal-00808015⟩



Record views