Fast Local Trust Region Technique for Diffusion Tensor Registration Using Exact Reorientation and Regularization

Diffusion tensor imaging is widely used in brain connectivity research. As more and more studies recruit large numbers of subjects, it is important to design registration methods which are not only theoretically rigorous, but also computationally efficient. However, the requirement of reorienting di...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2014-05, Vol.33 (5), p.1005-1022
Main Authors: Junning Li, Yonggang Shi, Giang Tran, Dinov, Ivo, Wang, Danny J. J., Toga, Arthur
Format: Article
Language:English
Subjects:
Algorithms
Approximation methods
Complexity theory
Diffeomorphisms
Diffusion
Diffusion rate
Diffusion tensor imaging
Diffusion Tensor Imaging - methods
Humans
Image Processing, Computer-Assisted - methods
Image registration
Linear systems
Mathematical analysis
Mathematical models
Optimization
partial differential equations
Regularization
Tensile stress
tensor reorientation
Tensors
trust region methods
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Summary:Diffusion tensor imaging is widely used in brain connectivity research. As more and more studies recruit large numbers of subjects, it is important to design registration methods which are not only theoretically rigorous, but also computationally efficient. However, the requirement of reorienting diffusion tensors complicates and considerably slows down registration procedures, due to the correlated impacts of registration forces at adjacent voxel locations. Based on the diffeomorphic Demons algorithm (Vercauteren , 2009), we propose a fast local trust region algorithm for handling inseparable registration forces for quadratic energy functions. The method guarantees that, at any time and at any voxel location, the velocity is always within its local trust region. This local regularization allows efficient calculation of the transformation update with numeric integration instead of completely solving a large linear system at every iteration. It is able to incorporate exact reorientation and regularization into the velocity optimization, and preserve the linear complexity of the diffeomorphic Demons algorithm. In an experiment with 84 diffusion tensor images involving both pair-wise and group-wise registrations, the proposed algorithm achieves better registration in comparison with other methods solving large linear systems (Yeo , 2009). At the same time, this algorithm reduces the computation time and memory demand tenfold.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2013.2274051