TY - GEN
T1 - System for live virtual-endoscopic guidance of bronchoscopy
AU - Helferty, J. P.
AU - Sherbondy, A. J.
AU - Kiraly, A. P.
AU - Higgins, W. E.
N1 - Funding Information:
This work was partially supported by NIH-NCI grants # R01-CA074325 and R44-CA091534. From Penn State, we would like to thank Janice Turlington, Allen Austin, David Zhang, Dirk Padfield, James Ross, Tao Yang, and Shu-Yen Wan, who contributed to the system and participated in some of the experiments. From the University of Iowa, we would like to thank Eric Hoffman, Geoffrey McLennan, Scott Ferguson, Karl Thomas, Alan Ross, Janice Cook-Granroth, Angela Delsing, Osama Saba, Deokiee Chon, Jered Sieren, and Curt Wolf, who participated in some of the experiments.
Publisher Copyright:
© 2005 IEEE Computer Society. All rights reserved.
PY - 2005
Y1 - 2005
N2 - The standard procedure for diagnosing lung cancer involves 3D computed-tomography (CT) assessment, followed by interventional bronchoscopy. In general, the physician has no link between the CT assessment results and the follow-on bronchoscopy. Thus, the physician essentially performs bronchoscopic biopsy of suspect cancer sites blindly. We have devised a computer-based system that greatly augments the physician's vision during bronchoscopy. The system uses techniques from computer graphics and computer vision to enable detailed 3D CT procedure planning and follow-on image-guided bronchoscopy. The procedure plan is directly linked to the bronchoscope procedure, through a live fusion of the 3D CT data and bronchoscopic video. During a procedure, the physician receives considerable visual feedback on how to maneuver the bronchoscope and where to insert the biopsy needle. We have performed a series of controlled phantom and animal tests, in addition to using the system on a large number of human lung-cancer patients. Results indicate that not only is the variation in skill level between different physicians greatly reduced, but that their accuracy increases.
AB - The standard procedure for diagnosing lung cancer involves 3D computed-tomography (CT) assessment, followed by interventional bronchoscopy. In general, the physician has no link between the CT assessment results and the follow-on bronchoscopy. Thus, the physician essentially performs bronchoscopic biopsy of suspect cancer sites blindly. We have devised a computer-based system that greatly augments the physician's vision during bronchoscopy. The system uses techniques from computer graphics and computer vision to enable detailed 3D CT procedure planning and follow-on image-guided bronchoscopy. The procedure plan is directly linked to the bronchoscope procedure, through a live fusion of the 3D CT data and bronchoscopic video. During a procedure, the physician receives considerable visual feedback on how to maneuver the bronchoscope and where to insert the biopsy needle. We have performed a series of controlled phantom and animal tests, in addition to using the system on a large number of human lung-cancer patients. Results indicate that not only is the variation in skill level between different physicians greatly reduced, but that their accuracy increases.
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U2 - 10.1109/CVPR.2005.538
DO - 10.1109/CVPR.2005.538
M3 - Conference contribution
AN - SCOPUS:33745333044
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
BT - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
PB - IEEE Computer Society
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
Y2 - 21 September 2005 through 23 September 2005
ER -
