TY - GEN
T1 - Affine coregistration of diffusion tensor magnetic resonance images using mutual information
AU - Leemans, Alexander
AU - Sijbers, Jan
AU - De Backer, Steve
AU - Vandervliet, Everhard
AU - Parizel, Paul M.
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multi-channel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appropriate reorientation of the diffusion tensor is worked out in order to remain consistent, with the corresponding underlying anatomical structures. This reorientation strategy is determined from the spatial transformation while preserving the diffusion tensor shape. The method is fully automatic and has the advantage to be independent of the applied diffusion framework.
AB - In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multi-channel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appropriate reorientation of the diffusion tensor is worked out in order to remain consistent, with the corresponding underlying anatomical structures. This reorientation strategy is determined from the spatial transformation while preserving the diffusion tensor shape. The method is fully automatic and has the advantage to be independent of the applied diffusion framework.
UR - https://www.scopus.com/pages/publications/33646199902
U2 - 10.1007/11558484_66
DO - 10.1007/11558484_66
M3 - Conference paper
AN - SCOPUS:33646199902
SN - 354029032X
SN - 9783540290322
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 523
EP - 530
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 7th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2005
Y2 - 20 September 2005 through 23 September 2005
ER -