Determination of Error in 3D CT to 2D Fluoroscopy Image Registration for Endobronchial Guidance

Abstract

Endobronchial biopsy is the preferred method for assessing lung lesions. However, navigation to pulmonary lesions and obtaining adequate tissue samples for diagnosis remains challenging. Utilizing information from high-resolution pre-procedural CT scans intra-procedurally could provide real-time guidance and confirmation during biopsy. An image registration algorithm was developed to automatically fuse thoracic 3D pre-operative CT images to 2D intra-procedural fluoroscopic images with a single 2D image or a limited C-arm sweep. A rigid intensity-based technique was applied and the CT image was iteratively transformed to minimize the sum of squared error between intraoperative fluoroscopy and closest forward projections. The registration errors were measured by computing the sum of squared difference and manually identified fiducial markers. In a swine model, error was minimized when using a CT with an inhalation breath hold ($7.7\pm 4.4$ mm) and when using an anterior-posterior positioning of the C-arm ($3.7\pm 2.4$ mm). Error increased marginally when the FOV was decreased ($10.9\pm 5.9$ mm) and was larger in peripheral ($9.7\pm 5.7$ mm) and distal ($9.2\pm 3.2$ mm) lung, compared to central ($6.2\pm 4.5$ mm) and proximal ($7.6\pm 5.9$ mm) lung. To determine the features that contribute most to registration, features were systematically masked and registration was performed. The largest error was seen when the spine was masked ($52.5\pm 27.6$ mm). When multiple images were used for registration, error converges (${<}5\%$ change) when 50 images acquired in a $100^{\circ }$ sweep were used. This work establishes a protocol and identifies sources of registration error for a reliable and automatic 2D-3D registration method that requires minimal changes to procedural workflow and equipment in the endobronchial suite.