SU-D-217A-05: Auto-Registration of Cardiac PET/CT Images with a 3D Weighted Gradient Correlation Algorithm

Medical Physics
H Ai, Tinsu Pan

Abstract

To design a novel 3D automatic registration algorithm for cardiac PET/CT registration using gradient information and to evaluate the performance of the algorithm with clinical PET/CT datasets. The 512×512×47 CT images are at first resized to 128×128×47 to match the matrix size of PET. In order to maximize the gradient information at boundaries in CT, a conventional fuzzy c-means clustering algorithm (number of cluster = 7) is implemented to suppress signals from tissues that do not contribute much useful information for the registration purpose (fat, lung, and bones). The 3D Image gradient map, consisting of all three orthogonal components, is derived from the PET images and the post- clustering CT images. The mis-registration is modeled as 3D rigid body translation in this study, though it can be extended to include rotations as well. The details of the gradient-based objective function are described in the support document. Optimal registration is determined by searching for the maximum of the objective function over a range of potential translation positions (e.g. 8.2×8.2×2.8cm). This process is repeated at a higher matrix size (512×512×47) to refine the result of registration. We applied this auto registration technique on ...Continue Reading

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