Interbronchoscopist variability in endobronchial path selection: a simulation study.

BACKGROUND: Endobronchial path selection is important for the bronchoscopic diagnosis of focal lung lesions. Path selection typically involves mentally reconstructing a three-dimensional path by interpreting a stack of two-dimensional (2D) axial plane CT scan sections. The hypotheses of our study about path selection were as follows: (1) bronchoscopists are inaccurate and overly confident when making endobronchial path selections based on 2D CT scan analysis; and (2) path selection accuracy and confidence improve and become better aligned when bronchoscopists employ path-planning methods based on virtual bronchoscopy (VB). METHODS: Studies of endobronchial path selection comparing three path-planning methods (ie, the standard 2D CT scan analysis and two new VB-based techniques) were performed. The task was to navigate to discrete lesions located between the third-order and fifth-order bronchi of the right upper and middle lobes. Outcome measures were the cumulative accuracy of making four sequential path selection decisions and self-reported confidence (1, least confident; 5, most confident). Both experienced and inexperienced bronchoscopists participated in the studies. RESULTS: In the first study involving a static paper-based tool, the mean (+/- SD) cumulative accuracy was 14 +/- 3% using 2D CT scan analysis (confidence, 3.4 +/- 1.3) and 49 +/- 15% using a VB-based technique (confidence, 4.2 +/- 1.1; p = 0.0001 across all comparisons). For a second study using an interactive computer-based tool, the mean accuracy was 40 +/- 28% using 2D CT scan analysis (confidence, 3.0 +/- 0.3) and 96 +/- 3% using a dynamic VB-based technique (confidence, 4.6 +/- 0.2). Regardless of the experience level of the bronchoscopist, use of the standard 2D CT scan analysis resulted in poor path selection accuracy and misaligned confidence. Use of the VB-based techniques resulted in considerably higher accuracy and better aligned decision confidence. CONCLUSIONS: Endobronchial path selection is a source of error in the bronchoscopy workflow. The use of VB-based path-planning techniques significantly improves path selection accuracy over use of the standard 2D CT scan section analysis in this simulation format.

Image-guided bronchoscopy for peripheral lung lesions: a phantom study.

BACKGROUND: Ultrathin bronchoscopy guided by virtual bronchoscopy (VB) techniques show promise for the diagnosis of peripheral lung lesions. In a phantom study, we evaluated a new real-time, VB-based, image-guided system for guiding the bronchoscopic biopsy of peripheral lung lesions and compared its performance to that of standard bronchoscopy practice. METHODS: Twelve bronchoscopists of varying experience levels participated in the study. The task was to use an ultrathin bronchoscope and a biopsy forceps to localize 10 synthetically created lesions situated at varying airway depths. For route planning and guidance, the bronchoscopists employed either standard bronchoscopy practice or the real-time image-guided system. Outcome measures were biopsy site position error, which was defined as the distance from the forceps contact point to the ground-truth lesion boundary, and localization success, which was defined as a site identification having a biopsy site position error of < or = 5 mm. RESULTS: Mean (+/- SD) localization success more than doubled from 43 +/- 16% using standard practice to 94 +/- 7.9% using image guidance (p < 10(-15) [McNemar paired test]). The mean biopsy site position error dropped from 9.7 +/- 9.1 mm for standard practice to 2.2 +/- 2.3 mm for image guidance. For standard practice, localization success decreased from 56% for generation 3 to 4 lesions to 31% for generation 6 to 8 lesions and also decreased from 51% for lesions on a carina vs 23% for lesions situated away from a carina. These factors were far less pronounced when using image guidance, as follows: success for generation 3 to 4 lesions, 97%; success for generation 6 to 8 lesions, 91%; success for lesions on a carina, 98%; success for lesions away from a carina, 86%. Bronchoscopist experience did not significantly affect performance using the image-guided system. CONCLUSIONS: Real-time, VB-based image guidance can potentially far exceed standard bronchoscopy practice for enabling the bronchoscopic biopsy of peripheral lung lesions.

Optimal procedure planning and guidance system for peripheral bronchoscopy.

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.

Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy.

A vital task in the planning of peripheral bronchoscopy is the segmentation of the airway tree from a 3-D multidetector computed tomography chest scan. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. We present a robust method that draws upon both local and global information. The method begins with a conservative segmentation of the major airways. Follow-on stages then exhaustively search for additional candidate airway locations. Finally, a graph-based optimization method counterbalances both the benefit and cost of retaining candidate airway locations for the final segmentation. Results demonstrate that the proposed method typically extracts 2-3 more generations of airways than several other methods, and that the extracted airway trees enable image-guided bronchoscopy deeper into the human lung periphery than past studies.