Optimal Procedure Planning and Guidance System for Peripheral Bronchoscopy

Optimal Procedure Planning and Guidance System for Peripheral Bronchoscopy 556 235 IEEE Transactions on Biomedical Engineering (TBME)

Jason D. Gibbs, Michael W. Graham, Rebecca Bascom, Duane C. Cornish, Rahul Khare, and William E. Higgins, Pennsylvania State University
Volume: 61, Issue, 3, Page: 638-657

Gibbs-BME-image March 2014

Summary: With the development of multi-detector computed tomography (MDCT) scanners and ultrathin bronchoscopes, the use of bronchoscopy for diagnosing peripheral lung-cancer nodules is becoming a viable option. The work flow for assessing lung cancer consists of two phases: (1) 3D MDCT analysis; and (2) live bronchoscopy. Unfortunately, the yield rates for peripheral bronchoscopy have been reported to be as low as 17%, and bronchoscopy performance varies considerably between physicians. Recently proposed image-guided systems have shown promise for assisting with peripheral bronchoscopy. Yet, MDCT-based route planning to target sites has relied on tedious error-prone techniques. In addition, route planning tends not to incorporate known anatomical, device, and procedural constraints that impact a feasible route. Finally, existing systems do not effectively integrate MDCT-derived route information into the live guidance process. We propose a system that incorporates an automatic optimal route-planning method, which integrates known route constraints. Furthermore, our system offers a natural translation of the MDCT-based route plan into the live guidance strategy via MDCT/video data fusion. An image-based study demonstrates the route-planning method’s functionality. Next, we present a prospective lung-cancer patient study in which our system achieved a successful navigation rate of 91% to target sites. Furthermore, when compared to a competing commercial system, our system enabled bronchoscopy over two airways deeper into the airway-tree periphery with a sample time that was nearly two minutes shorter on average. Finally, our system’s ability to almost perfectly predict the depth of a bronchoscope’s navigable route in advance represents a substantial benefit of optimal route planning.