| C-ARM POSE ESTIMATION AND NAVIGATION IN SURGERIES FOR AUGMENTED REALITY APPLICATION |
| Paper ID : 1285-SMPR |
| Authors: |
|
Sahar Hosseinian1, Hossein Arefi *2, Nassir Navab3 1University of Tehran-North Kargar street 2University of Tehran-North Kargar-Tehran 3TTechnical University of Munich (TUM) |
| Abstract: |
| C-arm X-ray imaging systems are widely applied in surgeries. Overlaying X-ray with optical images during the surgery has been shown to be an efficient approach. Moreover, overlaying needed data from different modalities in an augmented reality (AR) manner can improve the accuracy of surgical procedures, decrease the variability of surgical outcomes, reduce trauma to the critical structures, increase the reproducibility of surgeons’ performance, and reduce radiation exposure. C-Arm geometric calibration and recovering the C-arm pose are essential for surgical navigation and AR applications in operating rooms. Therefore, in this paper, existing researches for calibration and pose estimation of C-arm devices in surgical AR applications are evaluated from the photogrammetric point of view. Then, a proposed marker-based method for C-arm pose estimation is introduced. For this purpose, a marker is designed to facilitate tracking and pose estimation in mixed reality based on golden section principle, and perspective invariants such as cross ratios, collinearity, and intersection. Moreover, a procedure is also proposed for fast determination of these fiducial markers. The experiments show benefits of such a structure which has a limited occlusion with consistency to different conditions such as narrow field of view and at the same time, even in images with high projectivity. It has also resulted that the distortion correction step is important and the effect of distortion of X-ray images can cause inconsistency in the perspective invariants. |
| Keywords: |
| X-ray images, Geometric calibration, Image Guided Surgeries, Photogrammetry, Augmented Reality, Fiducial Markers, Surgery, cross-ratio |
| Status : Conditional Accept (Oral Presentation) |
