Review of Techniques for Registration of Diffusion Tensor Imaging

Image registration is a common image processing task, and therefore, many algorithms have been proposed and described to carry it out for different image modalities. However, the application of these algorithms to diffusion tensor imaging is not straightforward due to the special features of this kind of data, where a tensor is defined at each voxel. The information provided by the diffusion tensor is related to the anatomical structures in tissues, and this relation should be preserved, even though the image has been transformed by a registration procedure. On the other hand, the registration problem can be viewed as an optimization problem, where a similarity measure has to be maximized. The appropriate definition of this similarity measure is indeed an important issue for the registration of diffusion tensor images. In this paper, we compile the different approaches for the registration of diffusion tensor images that have been published. Special attention is paid to the aforementioned topics: how to preserve the coherence between the tensor and the underlying tissue structure, and how to measure the similarity between two diffusion tensors. Methods to evaluate results are also reviewed, since a reliable validation leads to more conclusive results, specially in the comparison of different techniques. Most challenging issues for diffusion tensor images registration are underlined, and open research lines about this topic are pointed out.