A projected landmark method for reduction of registration error in image-guided surgery systems
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Image-guided surgery systems are limited by registration error, so practical and effective methods to improve accuracy are necessary. A projection point-based method for reducing the surface registration error in image-guided surgery was developed and tested.
Checkerboard patterns are projected on visible surfaces to create projected landmarks over a region of interest. Surface information thus becomes available in the form of point clouds of surface point coordinates with submillimeter resolution. The reconstructed 3D point cloud is registered using iterative closest point (ICP) approximation to a 3D point cloud extracted from preoperative CT images of the same region of interest. The projected landmark surface registration method was compared with two other methods using a facial surface phantom: (a) landmark registration using anatomical features, and (b) surface matching based on an additional 40 surface points.
The mean error for the projected landmark surface registration method was 0.64 mm, which was 47.4 and 35.3 % lower relative to mean errors of the anatomical landmark registration and the surface-matching methods, respectively. After applying the proposed method, using target registration error as a gold standard, the resulting mean error was 1.1 mm or a reduction of 61.2 % compared to the anatomical landmark registration.
Optical checkerboard pattern projection onto visible surfaces was used to acquire surface point clouds for image-guided surgery registration. A projected landmark method eliminated the effects of unwanted and overlapping points by acquiring the desired points at specific locations. The results were more accurate than conventional landmark or surface registration.
KeywordsImage-guided surgery Target registration error Surface registration Projected landmarks
This Project was supported by the Research Center for Biomedical Technology and Robotics (RCBTR), Tehran, Iran and was filed as a provisional patent by the US Patent Office under Parsiss Co. license on August 20, 2012 (Application No.: 61691129; Confirmation No.: 7226).
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