A highly accurate, optical flow-based algorithm for nonlinear spatial normalization of diffusion tensor images

Spatial normalization plays a key role in voxel-based analyses of diffusion tensor images (DTI). We propose a highly accurate algorithm for high-dimensional spatial normalization of DTI data based on the technique of 3D optical flow. The theory of conventional optic flow assumes consistency of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering DTI data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is better not only than that of those traditional optical flow algorithms or using affine alignment, but also better than the results using popular tools such as the statistical parametric mapping (SPM) software package. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention.