Uniqueness of Solutions to Three Perspective Views of Four Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Automatic C-Arm Calibration for Fluoroscopy-Based Navigation: A Practical Approach
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
C-arm calibration – is it really necessary?
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast Marker Based C-Arm Pose Estimation
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Toward optimal matching for 3D reconstruction of brachytherapy seeds
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
IPCAI'10 Proceedings of the First international conference on Information processing in computer-assisted interventions
C-arm tracking by intensity-based registration of a fiducial in prostate brachytherapy
IPCAI'10 Proceedings of the First international conference on Information processing in computer-assisted interventions
C-arm pose estimation in prostate brachytherapy by registration to ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Prostate brachytherapy seed reconstruction using c-arm rotation measurement and motion compensation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
| Hi-index | 0.00 |
For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence and reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures. Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace. After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence and reconstruction. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.9o and a 3D reconstruction error of 0.8 mm, with an 8o region of convergence for both the AP and lateral axes. The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation and does not interfere with the workspace.