Construction of a multimodal CT-video chest model

Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscopea's continuous video stream as a guide, a physician can navigate through the lung airways to examine general airway health, collect tissue samples, or administer a disease treatment. In addition, physicians can now use new image-guided intervention (IGI) systems, which draw upon both three-dimensional (3D) multi-detector computed tomography (MDCT) chest scans and bronchoscopic video, to assist with bronchoscope navigation. Unfortunately, little use is made of the acquired video stream, a potentially invaluable source of information. In addition, little effort has been made to link the bronchoscopic video stream to the detailed anatomical information given by a patienta's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patienta's 3D MDCT-based airway-tree model, the method next parses the available video data to generate a positional linkage between a sparse set of key video frames and airway path locations. Next, a fusion/mapping of the videoa's color mucosal information and MDCT-based endoluminal surfaces is performed. This results in the final multimodal CT-video chest model. The data structure constituting the model provides a history of those airway locations visited during bronchoscopy. It also provides for quick visual access to relevant sections of the airway wall by condensing large portions of endoscopic video into representative frames containing important structural and textural information. When examined with a set of interactive visualization tools, the resulting fused data structure provides a rich multimodal data source. We demonstrate the potential of the multimodal model with both phantom and human data.