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In order to improve the clinical usefulness of computer-assisted fluoroscopic navigation, a new algorithm to automatically determine the calibration of fluoroscopic images has been developed. This is a challenging task since the intraoperative images acquired from fluoroscopic systems are often poor, making detection of the calibration grid difficult. Several feature-based methods have been implemented to perform bead detection for automatic detection of the calibration grids. The algorithms include support for multiple fields of view, a feature not supported on any computer assisted systems to date. In order to evaluate the performance of the algorithms, special phantoms were made and a cadaver study was performed to challenge the algorithms. One hundred images were acquired using three different C-Arms (OEC 9600, OEC 9800 and Philips BV-300+) using two different fields of view (nine and twelve inch). The chosen method successfully registered the images in ninety-six of the cases. The images that were not successfully registered were of limited clinical value anyway due to the very poor image quality.

Performance and Robustness of Automatic Fluoroscopic Image Calibration in a New Computer Assisted Surgery System