@article {Boctor2005,
	title = {Real-time quality control of tracked ultrasound},
	journal = {Medical image computing and computer-assisted intervention (MICCAI)},
	volume = {8},
	number = {Pt 1},
	year = {2005},
	pages = {621{\textendash}630},
	abstract = {<p>The overwhelming majority of intra-operative hazard situations in tracked ultrasound (US) systems are attributed to failure of registration between tracking, imaging coordinate frames We introduce a novel methodology for eal-time in-vivo quality control of tracked US systems, in order to capture registration failures during the clinical procedure In effect, we dynamically recalibrate the tracked US system for rotation, scale factor, and in-plane position offset up to a scale factor We detect any unexpected change in these parameters through capturing discrepancies in the resulting calibration matrix, thereby assuring quality (accuracy, consistency) of the tracked system No phantom is used for the recalibration We perform the task of quality control in the background, transparently to the clinical user while the subject is being scanned We present the concept, mathematical formulation, and experimental evaluation in-vitro This new method can play an important role in guaranteeing accurate, consistent, and reliable performance of tracked ultrasound</p>
},
	keywords = {Algorithms, Computer Systems, Computer-Assisted, Health Care, Humans, Image Enhancement, Image Interpretation, Interventional, methods, Quality Assurance, Quality Control, Reproducibility of Results, Sensitivity, Specificity, Surgery, Ultrasonography, User-Computer Interface},
	author = {Emad Boctor and Iulian Iordachita and Gabor Fichtinger and Gregory D. Hager}
}
@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}
}
