TY - GEN
T1 - Diffusion tensor image registration using tensor geometry and orientation features
AU - Yang, Jinzhong
AU - Shen, Dinggang
AU - Davatzikos, Christos
AU - Verma, Ragini
N1 - Funding Information:
This work was supported by the National Institute of Health via grants R01-MH-070365 and R01-MH-079938.
PY - 2008
Y1 - 2008
N2 - This paper presents a method for deformable registration of diffusion tensor (DT) images that integrates geometry and orientation features into a hierarchical matching framework. The geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. The orientation feature improves the matching of the WM fiber tracts by taking into account the statistical information of underlying fiber orientations. These features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. Extensive experiments on simulated and real brain DT data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation. The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes.
AB - This paper presents a method for deformable registration of diffusion tensor (DT) images that integrates geometry and orientation features into a hierarchical matching framework. The geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. The orientation feature improves the matching of the WM fiber tracts by taking into account the statistical information of underlying fiber orientations. These features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. Extensive experiments on simulated and real brain DT data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation. The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes.
KW - Attribute vector
KW - Deformable registration
KW - Diffusion tensor imaging
KW - Structural geometry
KW - Tensor orientation
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U2 - 10.1007/978-3-540-85990-1_109
DO - 10.1007/978-3-540-85990-1_109
M3 - Conference contribution
C2 - 18982691
AN - SCOPUS:84883846045
SN - 3540859896
SN - 9783540859895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 905
EP - 913
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
PB - Springer Verlag
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Y2 - 6 September 2008 through 10 September 2008
ER -