{"id":2406,"date":"2024-03-30T22:46:36","date_gmt":"2024-03-30T22:46:36","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/sangyoon-lee\/"},"modified":"2024-03-30T22:46:36","modified_gmt":"2024-03-30T22:46:36","slug":"sangyoon-lee","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/sangyoon-lee\/","title":{"rendered":"Sangyoon Lee"},"content":{"rendered":"
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Sangyoon Lee<\/h1>\n<\/div>\n
PhD Student<\/div>\n
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Johns Hopkins University<\/div>\n
Member from 2001<\/em> to 2002<\/em><\/div>\n
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Lee, Sangyoon; Fichtinger, Gabor; Chirikjian, Gregory<\/p>

Numerical algorithms for spatial registration of line fiducials from cross-sectional images Journal Article<\/span> <\/p>

In: <\/span>Journal of Medical Physics, <\/span>vol. 29, <\/span>no. 8, <\/span>pp. 1881\u20131891, <\/span>2002<\/span>.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{Lee2002,
\r\ntitle = {Numerical algorithms for spatial registration of line fiducials from cross-sectional images},
\r\nauthor = {Sangyoon Lee and Gabor Fichtinger and Gregory Chirikjian},
\r\nyear  = {2002},
\r\ndate = {2002-08-01},
\r\nurldate = {2002-08-01},
\r\njournal = {Journal of Medical Physics},
\r\nvolume = {29},
\r\nnumber = {8},
\r\npages = {1881\u20131891},
\r\nabstract = {<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>},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
<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><\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Lee, Sangyoon;  Fichtinger, Gabor;  Chirikjian, Gregory S<\/p>
Numerical algorithms for spatial registration of line fiducials from cross\u2010sectional images<\/a> Journal Article<\/span> <\/p>
In: <\/span>Medical Physics, <\/span>vol. 29, <\/span>iss. 8, <\/span>pp. 1881-1891, <\/span>2002<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{fichtinger2002e,
\r\ntitle = {Numerical algorithms for spatial registration of line fiducials from cross\u2010sectional images},
\r\nauthor = {Sangyoon Lee and Gabor Fichtinger and Gregory S Chirikjian},
\r\nurl = {https:\/\/aapm.onlinelibrary.wiley.com\/doi\/abs\/10.1118\/1.1493777},
\r\nyear  = {2002},
\r\ndate = {2002-01-01},
\r\njournal = {Medical Physics},
\r\nvolume = {29},
\r\nissue = {8},
\r\npages = {1881-1891},
\r\npublisher = {American Association of Physicists in Medicine},
\r\nabstract = {We present several numerical algorithms for six\u2010degree\u2010of\u2010freedom rigid\u2010body registration of line fiducial objects to their marks in cross\u2010sectional 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\u2010based 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\u2010assisted image\u2010guided surgical applications. We demonstrate the numerical methods on clinical CT images and computer\u2010generated 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\u2010thirds of the total fiducials are missing \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
We present several numerical algorithms for six\u2010degree\u2010of\u2010freedom rigid\u2010body registration of line fiducial objects to their marks in cross\u2010sectional 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\u2010based 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\u2010assisted image\u2010guided surgical applications. We demonstrate the numerical methods on clinical CT images and computer\u2010generated 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\u2010thirds of the total fiducials are missing \u2026<\/div>
Close<\/a><\/p><\/div>