Grid Enabled Non-rigid Registration with a Dense Transformation and a priori Information

Multi-subject non-rigid registration algorithms using dense transformations often encounter cases where the transformation to be estimated requires a large spatial variability. In these cases, linear regularization methods are not sufficient. In this paper, we present an algorithm that uses a priori information about the nature of the images in order to find more adapted deformations. We also present a robustness improvement that gives higher weight to those points in the images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. Results show that our method can take into account the large variability of the inner brain structures. A parallel implementation allowed us to execute the registration algorithm in 5 minutes and future improvements will open the possibility of registering massive quantities of images.