@article {Jain2006a, title = {C-arm tracking and reconstruction without an external tracker}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {9}, number = {Pt 1}, year = {2006}, pages = {494{\textendash}502}, publisher = {Department of Computer Science, Johns Hopkins University, USA}, abstract = {

For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence, 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, reconstruction Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0 9 degrees, a 3D reconstruction error of 0 8 mm, with an 80 region of convergence for both the AP, lateral axes The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation, does not interfere with the workspace

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods, methods, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional}, author = {Ameet K. Jain and Gabor Fichtinger} } @article {Jain2005, title = {C-arm calibration-is it really necessary?}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {8}, number = {Pt 1}, year = {2005}, pages = {639{\textendash}646}, abstract = {

C-arm fluoroscopy is modelled as a perspective projection, the parameters of which are estimated through a calibration procedure It has been universally accepted that precise intra-procedural calibration is a prerequisite for accurate quantitative C-arm fluoroscopy guidance Calibration, however, significantly adds to system complexity, which is a major impediment to clinical practice We challenge the status quo by questioning the assumption that precise intra-procedural calibration is really necessary We derived theoretical bounds for the sensitivity of 3D measurements to mis-calibration Experimental results corroborated the theory in that mis-calibration in the focal spot by as much as 50 mm still allows for tracking with an accuracy of 0 5 mm in translation, 0 65 degrees in rotation, and such mis-calibration does not impose any additional error on the reconstruction of small objects

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods/standards, methods/standards, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, United States}, author = {Ameet K. Jain and Ryan Kon and Yu Zhou and Gabor Fichtinger} } @article {Jain2005a, title = {FTRAC}, journal = {Journal of Medical Physics}, volume = {32}, number = {10}, year = {2005}, month = {Oct}, pages = {3185{\textendash}3198}, abstract = {

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional (3D) information A major obstacle in fluoroscopic reconstruction is discerning the pose of the x-ray image, in 3D space Optical/magnetic trackers tend to be prohibitively expensive, intrusive, cumbersome in many applications We present single-image-based fluoroscope tracking (FTRAC) with the use of an external radiographic fiducial consisting of a mathematically optimized set of ellipses, lines, and points This is an improvement over contemporary fiducials, which use only points The fiducial encodes six degrees of freedom in a single image by creating a unique view from any direction A nonlinear optimizer can rapidly compute the pose of the fiducial using this image The current embodiment has salient attributes: small dimensions (3 x 3 x 5 cm); need not be close to the anatomy of interest;, accurately segmentable We tested the fiducial, the pose recovery method on synthetic data, also experimentally on a precisely machined mechanical phantom Pose recovery in phantom experiments had an accuracy of 0 56 mm in translation, 0 33 degrees in orientation Object reconstruction had a mean error of 0 53 mm with 0 16 mm STD The method offers accuracies similar to commercial tracking systems, and appears to be sufficiently robust for intraoperative quantitative C-arm fluoroscopy Simulation experiments indicate that the size can be further reduced to 1 x 1 X 2 cm, with only a marginal drop in accuracy

}, keywords = {Algorithms, Computer-Assisted, Fluoroscopy, Imaging, Information Storage, instrumentation/methods, methods, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Retrieval, Sensitivity, Specificity, Three-Dimensional}, author = {Ameet K. Jain and Tabish Mustufa and Yu Zhou and E. Clif Burdette and Gregory Chirikjian and Gabor Fichtinger} }