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>},
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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 S
Numerical algorithms for spatial registration of line fiducials from cross‐sectional images Journal Article
In: Medical Physics, vol. 29, iss. 8, pp. 1881-1891, 2002.
@article{fichtinger2002e,
title = {Numerical algorithms for spatial registration of line fiducials from cross‐sectional images},
author = {Sangyoon Lee and Gabor Fichtinger and Gregory S Chirikjian},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.1493777},
year = {2002},
date = {2002-01-01},
journal = {Medical Physics},
volume = {29},
issue = {8},
pages = {1881-1891},
publisher = {American Association of Physicists in Medicine},
abstract = {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 and MRI, given the correspondence between the marks and line fiducials. The area of immediate application is frame‐based stereotactic procedures, such as radiosurgery and 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 and computer‐generated data and 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 …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 and MRI, given the correspondence between the marks and line fiducials. The area of immediate application is frame‐based stereotactic procedures, such as radiosurgery and 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 and computer‐generated data and 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 …
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}
}
Lee, Sangyoon; Fichtinger, Gabor; Chirikjian, Gregory S
Novel algorithms for robust registration of fiducials in CT and MRI Journal Article
In: pp. 717-724, 2001.
@article{fichtinger2001g,
title = {Novel algorithms for robust registration of fiducials in CT and MRI},
author = {Sangyoon Lee and Gabor Fichtinger and Gregory S Chirikjian},
url = {https://link.springer.com/chapter/10.1007/3-540-45468-3_86},
year = {2001},
date = {2001-01-01},
pages = {717-724},
publisher = {Springer Berlin Heidelberg},
abstract = {In this paper we present several numerical algorithms for registering fiducials in planar CT or MRI images to their corresponding three-dimensional locations. The unique strength of these methods is their ability to robustly handle incomplete fiducials patterns, even in extreme cases when as much as one third of the fiducial data is missing from the images. We compare the effectiveness of these algorithms in terms of flops and robustness on actual CT data sets.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper we present several numerical algorithms for registering fiducials in planar CT or MRI images to their corresponding three-dimensional locations. The unique strength of these methods is their ability to robustly handle incomplete fiducials patterns, even in extreme cases when as much as one third of the fiducial data is missing from the images. We compare the effectiveness of these algorithms in terms of flops and robustness on actual CT data sets.