Jason D. Gibbs

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.

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.

Robust system for human airway-tree segmentation

Robust and accurate segmentation of the human airway tree from multi-detector computed-tomography (MDCT) chest scans is vital for many pulmonary-imaging applications. As modern MDCT scanners can detect hundreds of airway tree branches, manual segmentation and semi-automatic segmentation requiring significant user intervention are impractical for producing a full global segmentation. Fully-automated methods, however, may fail to extract small peripheral airways. We propose an automatic algorithm that searches the entire lung volume for airway branches and poses segmentation as a global graph-theoretic optimization problem. The algorithm has shown strong performance on 23 human MDCT chest scans acquired by a variety of scanners and reconstruction kernels. Visual comparisons with adaptive region-growing results and quantitative comparisons with manually-defined trees indicate a high sensitivity to peripheral airways and a low false-positive rate. In addition, we propose a suite of interactive segmentation tools for cleaning and extending critical areas of the automatically segmented result. These interactive tools have potential application for image-based guidance of bronchoscopy to the periphery, where small, terminal branches can be important visual landmarks. Together, the automatic segmentation algorithm and interactive tool suite comprise a robust system for human airway-tree segmentation.

3D MDCT-based system for planning peripheral bronchoscopic procedures

The diagnosis and staging of lung cancer often begins with the assessment of a suspect peripheral chest site. Such suspicious peripheral sites may be solitary pulmonary nodules or other abnormally appearing regions of interest (ROIs). The state-of-the-art process for assessing such peripheral ROIs involves off-line procedure planning using a three-dimensional (3D) multidetector computed tomography (MDCT) chest scan followed by bronchoscopy with an ultrathin bronchoscope. We present an integrated computer-based system for planning peripheral bronchoscopic procedures. The system takes a 3D MDCT chest image as input and performs nearly all operations automatically. The only interaction required by the physician is the selection of ROI locations. The system is computationally efficient and fits smoothly within the clinical work flow. Integrated into the system and described in detail in the paper is a new surface-definition method, which is vital for effective analysis and planning to peripheral sites. Results demonstrate the efficacy of the system and its usage for the live guidance of ultrathin bronchoscopy to the periphery.

3D path planning and extension for endoscopic guidance

Physicians use endoscopic procedures to diagnose and treat a variety of medical conditions. For example, bronchoscopy is often performed to diagnose lung cancer. The current practice for planning endoscopic procedures requires the physician to manually scroll through the slices of a three-dimensional (3D) medical image. When doing this scrolling, the physician must perform 3D mental reconstruction of the endoscopic route to reach a specific diagnostic region of interest (ROI). Unfortunately, in the case of complex branching structures such as the airway tree, ROIs are often situated several generations away from the organs origin. Existing image-analysis methods can help define possible endoscopic navigation paths, but they do not provide specific routes for reaching a given ROI. We have developed an automated method to find a specific route to reach an ROI. Given a 3D medical image, our method takes as inputs: (1) pre-defined ROIs; (2) a segmentation of the branching organ through which the endoscopic device will navigate; and (3) centerlines (paths) through the segmented organ. We use existing methods for branching-organ segmentation and centerline extraction. Our method then (1) identifies the closest paths (routes) to the ROI; and (2) if necessary, performs a directed search for the organ of interest, extending the existing paths to complete a route. Results from human 3D computed tomography chest images illustrate the efficacy of the method.

Image-Based Reporting for Bronchoscopy

Bronchoscopy is often performed for staging lung cancer. The recent development of multidetector computed tomography (MDCT) scanners and ultrathin bronchoscopes now enable the bronchoscopic biopsy and treatment of peripheral diagnostic regions of interest (ROIs). Because these ROIs are often located several generations within the airway tree, careful planning and interpretation of the bronchoscopic route is required prior to a procedure. The current practice for planning bronchoscopic procedures, however, is difficult, error prone, and time consuming. To alleviate these issues, we propose a method for producing and previewing reports for bronchoscopic procedures using patient-specific MDCT chest scans. The reports provide quantitative data about the bronchoscopic routes and both static and dynamic previews of the proper airway route. The previews consist of virtual bronchoscopic endoluminal renderings along the route and three-dimensional cues for a final biopsy site. The reports require little storage space and computational resources, enabling physicians to view the reports on a portable tablet PC. To evaluate the efficacy of the reporting system, we have generated reports for 22 patients in a human lung cancer patient pilot study. For 17 of these patients, we used the reports in conjunction with live image-based bronchoscopic guidance to direct physicians to central chest and peripheral ROIs for subsequent diagnostic evaluation. Our experience shows that the tool enabled useful procedure preview and an effective means for planning strategy prior to a live bronchoscopy.

Method for continuous guidance of endoscopy

Previous research has indicated that use of guidance systems during endoscopy can improve the performance and decrease the skill variation of physicians. Current guidance systems, however, rely on computationally intensive registration techniques or costly and error-prone electromagnetic (E/M) registration techniques, neither of which fit seamlessly into the clinical workflow. We have previously proposed a real-time image-based registration technique that addresses both of these problems. We now propose a system-level approach that incorporates this technique into a complete paradigm for real-time image-based guidance in order to provide a physician with continuously-updated navigational and guidance information. At the core of the system is a novel strategy for guidance of endoscopy. Additional elements such as global surface rendering, local cross-sectional views, and pertinent distances are also incorporated into the system to provide additional utility to the physician. Phantom results were generated using bronchoscopy performed on a rapid prototype model of a human tracheobronchial airway tree. The system has also been tested in ongoing live human tests. Thus far, ten such tests, focused on bronchoscopic intervention of pulmonary patients, have been run successfully.

High yield of bronchoscopic transparenchymal nodule access real-time image-guided sampling in a novel model of small pulmonary nodules in canines.

BACKGROUND Bronchoscopic transparenchymal nodule access (BTPNA) is a novel approach to accessing pulmonary nodules. This real-time, image-guided approach was evaluated for safety, accuracy, and yield in the healthy canine model. METHODS A novel, inorganic model of subcentimeter pulmonary nodules was developed, consisting of 0.25-cc aliquots of calcium hydroxylapatite (Radiesse) implanted via transbronchial access in airways seven generations beyond the main bronchi to represent targets for evaluation of accuracy and yield. Thoracic CT scans were acquired for each subject, and from these CT scans LungPoint Virtual Bronchoscopic Navigation software provided guidance to the region of interest. Novel transparenchymal nodule access software algorithms automatically generated point-of-entry recommendations, registered CT images, and real-time fluoroscopic images and overlaid guidance onto live bronchoscopic and fluoroscopic video to achieve a vessel-free, straight-line path from a central airway through parenchymal tissue for access to peripheral lesions. RESULTS In a nine-canine cohort, the BTPNA procedure was performed to sample 31 implanted Radiesse targets, implanted to simulate pulmonary nodules, via biopsy forceps through a specially designed sheath. The mean length of the 31 tunnels was 35 mm (20.5-50.3-mm range). Mean tunnel creation time was 16:52 min, and diagnostic yield was 90.3% (28 of 31). No significant adverse events were noted in the status of any of the canine subjects post BTPNA, with no pneumothoraces and minimal bleeding (all bleeding events < 2 mL in volume). CONCLUSIONS These canine studies demonstrate that BTPNA has the potential to achieve the high yield of transthoracic needle aspiration with the low complication profile associated with traditional bronchoscopy. These results merit further study in humans.

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 methods 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 systems ability to almost perfectly predict the depth of a bronchoscopes navigable route in advance represents a substantial benefit of optimal route planning.

Feasibility and Safety of Bronchoscopic Transparenchymal Nodule Access in Canines: A New Real-Time Image-Guided Approach to Lung Lesions

BACKGROUND The current approaches for tissue diagnosis of a solitary pulmonary nodule are transthoracic needle aspiration, guided bronchoscopy, or surgical resection. The choice of procedure is driven by patient and radiographic factors, risks, and benefits. We describe a new approach to the diagnosis of a solitary pulmonary nodule, namely bronchoscopic transparenchymal nodule access (BTPNA). METHODS In anesthetized dogs, fiducial markers were placed and thoracic CT images acquired. From the CT scan, the BTPNA software provided automatic point-of-entry prescribing of a bronchoscopic path (tunnel) through parenchymal tissue directly to the lesion. The preplanned procedure was uploaded to a virtual bronchoscopic navigation system. Bronchoscopic access was performed through the tunnels created. Proximity of the distal end of the tunnel sheath to the target was measured, and safety was recorded. RESULTS In four canines, 13 tunnels were created. The average length of the tunnels was 32.3 mm (range, 24.7-46.7 mm). The average proximity measure was 5.7 mm (range, 0.1-12.9 mm). The distance from the pleura to the nearest point within the target was 7.4 mm (range, 0.1-15 mm). Estimated blood loss was <2 mL per case. There were no pneumothoraces. CONCLUSIONS We describe a new approach to accessing lesions in the lung parenchyma. BTPNA allows bronchoscopic creation of a direct path with a sheath placed in proximity to the target, creating the potential to deliver biopsy tools within a lesion to acquire tissue. The technology appears safe. Further experiments are needed to assess the diagnostic yield of this procedure in animals and, if promising, to assess this technology in humans.