Abstract
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.
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He, T. et al. (2010). A Minimally Invasive Multimodality Image-Guided (MIMIG) Molecular Imaging System for Peripheral Lung Cancer Intervention and Diagnosis. In: Navab, N., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2010. Lecture Notes in Computer Science, vol 6135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13711-2_10
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DOI: https://doi.org/10.1007/978-3-642-13711-2_10
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