Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images

• Published in: IEEE transactions on medical imaging Vol. 27; no. 12; pp. 1704 - 1714
• Main Authors: Markelj, P., Tomazevic, D., Pernus, F., Likar, B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomical structure; Anatomy; Computed tomography; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; Image registration; Image segmentation; Image-guided; Imaging, Three-Dimensional - methods; intensity gradients; intervention; Iterative algorithms; Magnetic resonance; Magnetic Resonance Imaging - methods; Normal Distribution; Robustness; Studies; surgery; three-dimensional/two-dimensional (3-D/2-D) registration; Tomography, X-Ray Computed - methods; X-ray imaging; 3-D/2-D registration; intervention; image-guided; surgery; intensity gradients
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2008.923984

One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.