Fast reconstruction of image deformation field using radial basis function

Fast and accurate registration of image data is a key component of computer-aided medical image analysis. Instead of performing the registration directly on the input images, many algorithms compute the transformation using a sparse representation extracted from the original data. However, in order to apply the resulting transformation onto the original images, a dense deformation field has to be reconstructed using a suitable inter-/extra-polation technique. In this paper, we employ the radial basis function (RBF) to reconstruct the dense deformation field from a sparse transformation computed by a model-based registration. Various kernels are tested using different scenario. The dense deformation field is used to warp the source image and compare it quantitatively to the target image using two different metrics. Moreover, the influence of the number and distribution of the control points required by the RBF is studied via two different scenarios. Beside the accuracy, the performance of the method accelerated using a GPU is reported.