Fast and Robust 3D to 2D Image Registration by Backprojection of Gradient Covariances

Abstract

Visualization and analysis of intra-operative images in imageguided radiotherapy and surgery are mainly limited to 2D X-ray imaging, which could be beneficially fused with information-rich pre-operative 3D image information by means of 3D-2D image registration. To keep the radiation dose delivered by the X-ray system low, the intra-operative imaging is usually limited to a single projection view. Registration of 3D to a single 2D image is a very challenging registration task for most of current state-of-the-art 3D-2D image registration methods. We propose a novel 3D-2D rigid registration method based on evaluation of similarity between corresponding 3D and 2D gradient covariances, which are mapped into the same space using backprojection. Normalized scalar product of covariances is computed as similarity measure. Performance of the proposed and state-of-the-art 3D-2D image registration methods was evaluated on two publicly available image datasets, one of cerebral angiograms and the other of a spine cadaver, using standardized evaluation methodology. Results showed that the proposed method outperformed the current state-of-the-art methods and achieved registration accuracy of 0.5 mm, capture range of 9 mm and success rate >80%. Considering also that GPU-enabled execution times ranged from 0.5-2.0 seconds, the proposed method has the potential to enhance with 3D information the visualization and analysis of intra-operative 2D images.