Sangyoon Lee
Graduate student (PhD)
Mechanical Engineering
Johns Hopkins University
Lee, Sangyoon; Fichtinger, Gabor; Chirikjian, Gregory
Numerical algorithms for spatial registration of line fiducials from cross-sectional images Journal Article
In: Journal of Medical Physics, vol. 29, no. 8, pp. 1881–1891, 2002.
@article{Lee2002,
title = {Numerical algorithms for spatial registration of line fiducials from cross-sectional images},
author = {Sangyoon Lee and Gabor Fichtinger and Gregory Chirikjian},
year = {2002},
date = {2002-08-01},
urldate = {2002-08-01},
journal = {Journal of Medical Physics},
volume = {29},
number = {8},
pages = {1881–1891},
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 = {},
pubstate = {published},
tppubtype = {article}
}
<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>
Lee, Sangyoon; Fichtinger, Gabor; Chirikjian, Gregory
Novel Algorithms for Robust Registration of Fiducials in CT and MRI Journal Article
In: Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2001, vol. 2208, pp. 717-724, 2001.
@article{Lee2001,
title = {Novel Algorithms for Robust Registration of Fiducials in CT and MRI},
author = {Sangyoon Lee and Gabor Fichtinger and Gregory Chirikjian},
year = {2001},
date = {2001-01-01},
journal = {Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2001},
volume = {2208},
pages = {717-724},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lee, Sangyoon; Fichtinger, Gabor; Chirikjian, Gregory
Novel Algorithms for Robust Registration of Fiducials in CT and MRI Journal Article
In: Journal of Medical Physics, vol. 29, pp. 1881-1891, 2001.
@article{Lee2001a,
title = {Novel Algorithms for Robust Registration of Fiducials in CT and MRI},
author = {Sangyoon Lee and Gabor Fichtinger and Gregory Chirikjian},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Lee2001.pdf},
year = {2001},
date = {2001-01-01},
urldate = {2001-01-01},
journal = {Journal of Medical Physics},
volume = {29},
pages = {1881-1891},
keywords = {},
pubstate = {published},
tppubtype = {article}
}