TY - GEN
T1 - A minimally invasive multimodality image-guided (MIMIG) molecular imaging system for peripheral lung cancer intervention and diagnosis
AU - He, Tiancheng
AU - Xue, Zhong
AU - Wong, Kelvin K.
AU - Alvarado, Miguel Valdivia Y
AU - Zhang, Yong
AU - Xie, Weixin
AU - Wong, Stephen T C
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The once-promising computed tomography (CT) lung cancer screening appears to result in high false positive rates. To tackle the common difficulties in diagnosing small lung cancer at an early stage, we developed a minimally invasive multimodality image-guided (MIMIG) interventional system for early detection and treatment of peripheral lung cancer. The system consists of new CT image segmentation for surgical planning, intervention guidance for targeting, and molecular imaging for diagnosis. Using advanced image segmentation technique the pulmonary vessels, airways, as well as nodules can be better visualized for surgical planning. These segmented results are then transformed onto the intra-procedural CT for interventional guidance using electromagnetic (EM) tracking. Diagnosis can be achieved at microscopic resolution using a fiber-optic microendoscopy. The system can also be used for fine needle aspiration biopsy to improve the accuracy and efficiency. Confirmed cancer could then be treated on-the-spot using radio-frequency ablation (RFA). The experiments on rabbits with VX2 lung cancer model show both accuracy and efficiency in localization and detecting lung cancer, as well as promising molecular imaging tumor detection.
AB - The once-promising computed tomography (CT) lung cancer screening appears to result in high false positive rates. To tackle the common difficulties in diagnosing small lung cancer at an early stage, we developed a minimally invasive multimodality image-guided (MIMIG) interventional system for early detection and treatment of peripheral lung cancer. The system consists of new CT image segmentation for surgical planning, intervention guidance for targeting, and molecular imaging for diagnosis. Using advanced image segmentation technique the pulmonary vessels, airways, as well as nodules can be better visualized for surgical planning. These segmented results are then transformed onto the intra-procedural CT for interventional guidance using electromagnetic (EM) tracking. Diagnosis can be achieved at microscopic resolution using a fiber-optic microendoscopy. The system can also be used for fine needle aspiration biopsy to improve the accuracy and efficiency. Confirmed cancer could then be treated on-the-spot using radio-frequency ablation (RFA). The experiments on rabbits with VX2 lung cancer model show both accuracy and efficiency in localization and detecting lung cancer, as well as promising molecular imaging tumor detection.
KW - Image computing
KW - Image-guided intervention
KW - Molecular imaging
KW - Peripheral lung cancer
UR - http://www.scopus.com/inward/record.url?scp=78049437434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049437434&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13711-2_10
DO - 10.1007/978-3-642-13711-2_10
M3 - Conference contribution
AN - SCOPUS:78049437434
SN - 3642137105
SN - 9783642137105
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 102
EP - 112
BT - Information Processing in Computer-Assisted Interventions - First International Conference, IPCAI 2010, Proceedings
T2 - 1st International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2010
Y2 - 23 June 2010 through 23 June 2010
ER -