Bronchoscopic biopsy of the central-chest lymph nodes is an important step for lung-cancer staging. Before bronchoscopy, the physician first visually assesses a patient's three-dimensional (3D) computed tomography (CT) chest scan to identify suspect lymph-node sites. Next, during bronchoscopy, the physician guides the bronchoscope to each desired lymph-node site. Unfortunately, the physician has no link between the 3D CT image data and the live video stream provided during bronchoscopy. Thus, the physician must essentially perform biopsy blindly, and the skill levels between different physicians differ greatly. We describe an approach that enables synergistic fusion between the 3D CT data and the bronchoscopic video. Both the integrated planning and guidance system and the internal CT-video registration and fusion methods are described. Phantom, animal, and human studies illustrate the efficacy of the methods.

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Objective Three-dimensional (3D) computed-tomography (CT) images and bronchoscopy are commonly used tools for the assessment of lung cancer. Before bronchoscopy, the physician first examines a 3D CT chest image to select pertinent diagnostic sites and to ascertain possible routes through the airway tree leading to each site. Next, during bronchoscopy, the physician maneuvers the bronchoscope through the airways, basing navigation decisions on the live bronchoscopic video, to reach each diagnostic site. Unfortunately, no direct link exists between the 3D CT image data and bronchoscopic video. This makes bronchoscopy difficult to perform successfully. Existing methods for the image-based guidance of bronchoscopy, either only involve single-frame registration or operate at impractically slow frame rates. We describe a method that combines the 3D CT image data and bronchoscopic video to enable continuous bronchoscopic guidance. Methods The method interleaves periodic CT-video registration with bronchoscopic video motion tracking. It begins by using single-frame CT-video registration to register the "Real World" of the bronchoscopic video and the "Virtual World" of the CT-based endoluminal renderings. Next, the method uses an optical-flow-based approach to track bronchoscope movement for a fixed number of video frames and also simultaneously updates the virtual-world position. This process of registration and tracking repeats as the bronchoscope moves.Results The method operates robustly over a variety of phantom and human cases. It typically performs successfully for over 150 frames and through multiple airway generations. In our tests, the method runs at a rate of up to seven frames per second. We have integrated the method into a complete system for the image-based planning and guidance of bronchoscopy. Conclusion The method performs over an order of magnitude faster than previously proposed image-based bronchoscopy guidance methods. Software optimization and a modest hardware improvement could enable real-time performance.

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