Optimal Procedure Planning and Guidance System for Peripheral Bronchoscopy

Abstract

With the development of multidetector 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) 3-D MDCT analysis and 2) live bronchoscopy. Unfortunately, the yield rates for peripheral bronchoscopy have been reported to be as low as 14%, 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 2 min 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.

Construction of a multimodal CT-video chest model

“…MDCT Analysis: Use existing methods to automatically segment the 3D airway tree, define the airway endoluminal surfaces, and compute the airway centerlines. [27][28][29][30] This gives an MDCT-based chest model. The final output consists of a multimodal data structure that establishes linkages between the video data and specific 3D locations within the MDCT-based chest model.…”

“…First, Bronchoscopy Path-History Construction employs the processing flow below: (a) Interactively locate an MDCT-based endoluminal rendering I Θ CT that appears to be at approximately the same airway-tree pose as I K (i), where Θ is the standard 6-parameter pose vector indicating the virtual camera's field of view within the MDCT-based airway tree. 8,30 (b) Apply CT-video registration to the image pair {I Θ CT , I K (i)} to arrive at a pose Θ i that aligns the MDCT-based virtual space and real space (as imaged by the video); i.e., after registration, the following relationship holds:…”

“…Since the tree structure produced by these methods involves an ordered hierarchy of airway branches and associated view sites, the computation of the bronchoscopy path history is straightforward. 30 CT-video registration (step 1. (b)) uses the heavily validated method of Merritt et al 8 Finally, in CT-Video Model Construction, we use the previously developed methodology of Rai and Higgins for video-to-CT texture mapping.…”

“…The system builds upon our previous CT-video efforts described by Higgins et al 7,8,27,30 In particular, we augment this system to allow for the storage and interaction of all data elements in the model. Next, we have made initial prototypes of the following tools: 1.…”

Image-guided endobronchial ultrasound

“…4,5,7,8 With such a system, a patient's X-ray computed tomography (CT) scan is used to plan a procedure to regions of interest (ROIs); this plan is then used during follow-on guided bronchoscopy. Recent clinical guidelines for lung cancer, however, now also dictate using positron emission tomography (PET) imaging for identifying suspicious ROIs and aiding in the cancer-staging process.…”