Point Similarity Measures Based on MRF Modeling of Difference Images for Spline-Based 2D-3D Rigid Registration of X-Ray Fluoroscopy to CT Images

One of the main factors that affect the accuracy of intensity-based registration of two-dimensional (2D) X-ray fluoroscopy to three-dimensional (3D) CT data is the similarity measure, which is a criterion function that is used in the registration procedure for measuring the quality of image match. This paper presents a unifying framework for rationally deriving point similarity measures based on Markov random field (MRF) modeling of difference images which are obtained by comparing the reference fluoroscopic images with their associated digitally reconstructed radiographs (DRR's). The optimal solution is defined as the maximum a posterior (MAP) estimate of the MRF. Three novel point similarity measures derived from this framework are presented. They are evaluated using a phantom and a human cadaveric specimen. Combining any one of the newly proposed similarity measures with a previously introduced spline-based registration scheme, we develop a fast and accurate registration algorithm. We report their capture ranges, converging speeds, and registration accuracies.