Image-guided bronchoscopy is a critical component in the treatment of lung cancer and other pulmonary disorders. During bronchoscopy, a high-resolution endobronchial video stream facilitates guidance through the lungs and allows for visual inspection of a patient’s airway mucosal surfaces. Despite the detailed information it contains, little effort has been made to incorporate recorded video into the clinical workflow. Follow-up procedures often required in cancer assessment or asthma treatment could significantly benefit from effectively parsed and summarized video. Tracking diagnostic regions of interest (ROIs) could potentially better equip physicians to detect early airway-wall cancer or improve asthma treatments, such as bronchial thermoplasty. To address this need, we have developed a system for the postoperative analysis of recorded endobronchial video. The system first parses an input video stream into endoscopic shots, derives motion information, and selects salient representative key frames. Next, a semi-automatic method for CT-video registration creates data linkages between a CT-derived airway-tree model and the input video. These data linkages then enable the construction of a CT-video chest model comprised of a bronchoscopy path history (BPH) - defining all airway locations visited during a procedure - and texture-mapping information for rendering registered video frames onto the airwaytree model. A suite of analysis tools is included to visualize and manipulate the extracted data. Video browsing and retrieval is facilitated through a video table of contents (TOC) and a search query interface. The system provides a variety of operational modes and additional functionality, including the ability to define regions of interest. We demonstrate the potential of our system using two human case study examples.
Endobronchial ultrasound (EBUS) is now recommended as a standard procedure for in vivo verification of extraluminal diagnostic sites during cancer-staging bronchoscopy. Yet, physicians vary considerably in their skills at using EBUS effectively. Regarding existing bronchoscopy guidance systems, studies have shown their effectiveness in the lung-cancer management process. 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, also dictate using positron emission tomography (PET) imaging for identifying suspicious ROIs and aiding in the cancer-staging process. While researchers have attempted to use guided bronchoscopy systems in tandem with PET imaging and EBUS, no true EBUS-centric guidance system exists. We now propose a full multimodal image-based methodology for guiding EBUS. The complete methodology involves two components: 1) a procedure planning protocol that gives bronchoscope movements appropriate for live EBUS positioning; and 2) a guidance strategy and associated system graphical user interface (GUI) designed for image-guided EBUS. We present results demonstrating the operation of the system.
Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic- shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.
Many technical innovations in multimodal radiologic imaging and bronchoscopy have emerged recently in the effort against lung cancer. Modern X-ray computed-tomography (CT) scanners provide three-dimensional (3D) high-resolution chest images, positron emission tomography (PET) scanners give complementary molecular imaging data, and new integrated PET/CT scanners combine the strengths of both modalities. State-of-the-art bronchoscopes permit minimally invasive tissue sampling, with vivid endobronchial video enabling navigation deep into the airway-tree periphery, while complementary endobronchial ultrasound (EBUS) reveals local views of anatomical structures outside the airways. In addition, image-guided intervention (IGI) systems have proven their utility for CT-based planning and guidance of bronchoscopy. Unfortunately, no IGI system exists that integrates all sources effectively through the complete lung-cancer staging work flow. This paper presents a prototype of a computer-based multimodal IGI system that strives to fill this need. The system combines a wide range of automatic and semi-automatic image-processing tools for multimodal data fusion and procedure planning. It also provides a flexible graphical user interface for follow-on guidance of bronchoscopy/EBUS. Human-study results demonstrate the system’s potential.
Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscope’s continuous video stream as a guide, a physician can navigate through the lung airways to examine general airway health, collect tissue samples, or administer a disease treatment. In addition, physicians can now use new image-guided intervention (IGI) systems, which draw upon both three-dimensional (3D) multi-detector computed tomography (MDCT) chest scans and bronchoscopic video, to assist with bronchoscope navigation. Unfortunately, little use is made of the acquired video stream, a potentially invaluable source of information. In addition, little effort has been made to link the bronchoscopic video stream to the detailed anatomical information given by a patient’s 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patient’s 3D MDCT-based airway-tree model, the method next parses the available video data to generate a positional linkage between a sparse set of key video frames and airway path locations. Next, a fusion/mapping of the video’s color mucosal information and MDCT-based endoluminal surfaces is performed. This results in the final multimodal CT-video chest model. The data structure constituting the model provides a history of those airway locations visited during bronchoscopy. It also provides for quick visual access to relevant sections of the airway wall by condensing large portions of endoscopic video into representative frames containing important structural and textural information. When examined with a set of interactive visualization tools, the resulting fused data structure provides a rich multimodal data source. We demonstrate the potential of the multimodal model with both phantom and human data.