Fast diffusion tensor registration with exact reorientation and regularization

Authors:
Junning Li;Yonggang Shi;Giang Tran;Ivo Dinov;Danny J. J. Wang;Arthur W. Toga
Affiliations:
Laboratory of Neuro Imaging, University of California, Los Angeles, CA;Laboratory of Neuro Imaging, University of California, Los Angeles, CA;Department of Mathematics, University of California, Los Angeles, CA;Laboratory of Neuro Imaging, University of California, Los Angeles, CA;Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA;Laboratory of Neuro Imaging, University of California, Los Angeles, CA
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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Abstract

Diffusion tensor imaging is widely used in brain connectivity study. As more and more group studies recruit a large number of subjects, it is important to design registration methods that are not only theoretically rigorous, but also computationally efficient, for processing large data sets. However, the requirement of reorienting diffusion tensors complicates and slows down the registration, especially for those methods whose scalar-image versions have linear complexity, for example, the Demons algorithm. In this paper, we propose an extension of the Demons algorithm that incorporates exact reorientation and regularization into the calculation of deforming velocity, yet preserving its linear complexity. This method restores the computational efficiency of the Demons algorithm to diffusion images, but does not sacrifice registration goodness. In our experiments, the new algorithm achieved state-of-art performance at a ten-fold decrease of computational time.