Computer-Based Route-Definition System for Peripheral Bronchoscopy

IEEE Trans. Biomed. Eng.

et al. 2014

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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.

Section: Discussionmentioning

confidence: 99%

Optimal Procedure Planning and Guidance System for Peripheral Bronchoscopy

Gibbs

Graham

IEEE Trans. Biomed. Eng.

et al. 2014

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“…We did not use our system during these phases of a patient procedure. As a standard practice, the physician reviewed the preplanned routes for each case before the start of bronchoscopy [16], [44], [45]. In addition, we recorded all procedures, including the VBN system’s GUI display, to assist with data analysis afterward.…”

Section: Resultsmentioning

confidence: 99%

“…Using the system, the physician previewed a “VB” by playing a cine movie of the VB view sequence suggested by the CT-derived route [16], [44], [45]. While this preview nominally illustrates the proper bronchoscope trajectory to execute during later live navigation, two issues remain.…”

Section: Methodsmentioning

confidence: 99%

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Hands-Free System for Bronchoscopy Planning and Guidance

Khare

IEEE Trans. Biomed. Eng.

Higgins

2015

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Bronchoscopy is a commonly usedminimally invasive procedure for lung-cancer staging. In standard practice, however, physicians differ greatly in their levels of performance. To address this concern, image-guided intervention (IGI) systems have been devised to improve procedure success. Current IGI bronchoscopy systems based on virtual bronchoscopic navigation (VBN), however, require involvement from the attending technician. This lessens physician control and hinders the overall acceptance of such systems. We propose a hands-free VBN system for planning and guiding bronchoscopy. The system introduces two major contributions. First, it incorporates a new procedure-planning method that automatically computes airway navigation plans conforming to the physician’s bronchoscopy training and manual dexterity. Second, it incorporates a guidance strategy for bronchoscope navigation that enables user-friendly system control via a foot switch, coupled with a novel position-verification mechanism. Phantom studies verified that the system enables smooth operation under physician control, while also enabling faster navigation than an existing technician-assisted VBN system. In a clinical human study, we noted a 97% bronchoscopy navigation success rate, in line with existing VBN systems, and a mean guidance time per diagnostic site= 52 s. This represents a guidance time often nearly 3 min faster per diagnostic site than guidance times reported for other technician-assisted VBN systems. Finally, an ergonomic study further asserts the system’s acceptability to the physician and long-term potential.

“…Results showed that image-based guidance significantly improves peripheral-lung biopsy performance over standard practice from 43% to 94% [9]. In addition, several other studies have shown that the system fits smoothly within the standard two-step lung cancer staging work flow [46], [50], [51], [68]. For example, we have applied the system to 30 patients who underwent peripheral bronchoscopy (age range: 18–85 years; 9 males, 21 females).…”

Section: Resultsmentioning

confidence: 99%

Interactive CT-Video Registration for the Continuous Guidance of Bronchoscopy

Merritt¹,

Khare

IEEE Trans. Med. Imaging

et al. 2013

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Bronchoscopy is a major step in lung cancer staging. To perform bronchoscopy, the physician uses a procedure plan, derived from a patient’s 3D computed-tomography (CT) chest scan, to navigate the bronchoscope through the lung airways. Unfortunately, physicians vary greatly in their ability to perform bronchoscopy. As a result, image-guided bronchoscopy systems, drawing upon the concept of CT-based virtual bronchoscopy (VB), have been proposed. These systems attempt to register the bronchoscope’s live position within the chest to a CT-based virtual chest space. Recent methods, which register the bronchoscopic video to CT-based endoluminal airway renderings, show promise but do not enable continuous real-time guidance. We present a CT-video registration method inspired by computer-vision innovations in the fields of image alignment and image-based rendering. In particular, motivated by the Lucas–Kanade algorithm, we propose an inverse-compositional framework built around a gradient-based optimization procedure. We next propose an implementation of the framework suitable for image-guided bronchoscopy. Laboratory tests, involving both single frames and continuous video sequences, demonstrate the robustness and accuracy of the method. Benchmark timing tests indicate that the method can run continuously at 300 frames/s, well beyond the real-time bronchoscopic video rate of 30 frames/s. This compares extremely favorably to the ≥1 s/frame speeds of other methods and indicates the method’s potential for real-time continuous registration. A human phantom study confirms the method’s efficacy for real-time guidance in a controlled setting, and, hence, points the way toward the first interactive CT-video registration approach for image-guided bronchoscopy. Along this line, we demonstrate the method’s efficacy in a complete guidance system by presenting a clinical study involving lung cancer patients.