@article {Jain2005a,
	title = {FTRAC},
	journal = {Journal of Medical Physics},
	volume = {32},
	number = {10},
	year = {2005},
	month = {Oct},
	pages = {3185{\textendash}3198},
	abstract = {<p>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</p>
},
	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}
}
@article {Lee2002,
	title = {Numerical algorithms for spatial registration of line fiducials from cross-sectional images},
	journal = {Journal of Medical Physics},
	volume = {29},
	number = {8},
	year = {2002},
	month = {Aug},
	pages = {1881{\textendash}1891},
	abstract = {<p>We present several numerical algorithms for six-degree-of-freedom rigid-body registration of line fiducial objects to their marks in cross-sectional planar images, such as those obtained in CT, MRI, given the correspondence between the marks, line fiducials The area of immediate application is frame-based stereotactic procedures, such as radiosurgery, functional neurosurgery The algorithms are also suitable to problems where the fiducial pattern moves inside the imager, as is the case in robot-assisted image-guided surgical applications We demonstrate the numerical methods on clinical CT images, computer-generated data, compare their performance in terms of robustness to missing data, robustness to noise, and speed The methods show two unique strengths: (1) They provide reliable registration of incomplete fiducial patterns when up to two-thirds of the total fiducials are missing from the image;, (2) they are applicable to an arbitrary combination of line fiducials without algorithmic modification The average speed of the fastest algorithm is 0 3236 s for six fiducial lines in real CT data in a Matlab implementation</p>
},
	keywords = {Algorithms, Anatomy, anatomy /\&/ histology/radiography, Cross-Sectional, Head, Humans, Image Enhancement, Imaging, Magnetic Resonance Imaging, methods, Quality Control, Reproducibility of Results, Sensitivity, Specificity, Stereotaxic Techniques, Stochastic Processes, Subtraction Technique, Three-Dimensional, Tomography, X-Ray Computed},
	author = {Sangyoon Lee and Gabor Fichtinger and Gregory Chirikjian}
}
