@article {DiMaio2007,
	title = {Dynamic MRI scan plane control for passive tracking of instruments and devices},
	journal = {Medical image computing and computer-assisted intervention (MICCAI)},
	volume = {10},
	number = {Pt 2},
	year = {2007},
	pages = {50{\textendash}58},
	publisher = {Brigham, Women{\textquoteright}s Hospital, Harvard Medical School, Boston, MA, USA},
	abstract = {<p>This paper describes a novel image-based method for tracking robotic mechanisms, interventional devices during Magnetic Resonance Image (MRI)-guided procedures It takes advantage of the multi-planar imaging capabilities of MRI to optimally image a set of localizing fiducials for passive motion tracking in the image coordinate frame The imaging system is servoed to adaptively position the scan plane based on automatic detection, localization of fiducial artifacts directly from the acquired image stream This closed-loop control system has been implemented using an open-source software framework, currently operates with GE MRI scanners Accuracy, performance were evaluated in experiments, the results of which are presented here</p>
},
	keywords = {Algorithms, Anatomy, Artificial Intelligence, Automated, Computer-Assisted, Cross-Sectional, Humans, Image Enhancement, Image Interpretation, Imaging, instrumentation/methods, Magnetic Resonance Imaging, methods, Pattern Recognition, Phantoms, Reproducibility of Results, Robotics, Sensitivity, Specificity, Surgery, Three-Dimensional, User-Computer Interface},
	author = {Simon DiMaio and Eigil Samset and Gregory Fischer and Iulian Iordachita and Gabor Fichtinger and Ferenc Jolesz and Clare M Tempany}
}
@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}
}
