Longitudinal changes in the corpus callosum following pediatric traumatic brain injury.
Intensity value-based registration is a widely used technique for the spatial alignment of medical images. Generally, the registration transformation is determined by iteratively optimizing a similarity measure calculated from the grey values of both images. However, such algorithms may have high computational costs, especially in the case of multi-modality registration, which makes their integration into systems difficult. At present, registration based on mutual information (MI) still requires computation times of the order of several minutes. In this contribution we focus on a new similarity measure based on local correlation (LC) which is well-suited for numerical optimization. We show that LC can be formulated as a least-squares criterion which allows the use of dedicated methods. Thus, it is possible to register MR neuro perfusion time-series (128 30 voxel, 40 images) on a moderate workstation in real-time: the registration of an image takes about 500 ms and is therefore several times faster than image acquisition time. For the registration of CT–MR images (512 87CT, 256 128 MR) a multiresolution framework is used. On top of the decomposition, which requires 47 s of computation time, the optimization with an algorithm based on MI previously described in the literature takes 97 s. In contrast, the proposed approach only takes 13 s, corresponding to a speedup about a factor of 7. Furthermore, we demonstrate that the superior computational performance of LC is not gained at the expense of accuracy. In particular, experiments with dual contrast MR images providing ground truth for the registration show a comparable sub-voxel accuracy of LC and MI similarity.