The goal of this project is to construct a multimodal image-guided intervention (IGI) system for the planning and guidance of bronchoscopy. The system will have the following features: 1) be easy to use, independent of physician skill and experience;2) enable better-focused, confident tissue sampling, with fewer patient complications;3) offer robust guidance;and 4) better exploit the available image sources. Lung cancer is the leading cause of cancer death worldwide. Many technical innovations in multimodal radiologic imaging and bronchoscopy have emerged recently in the effort against lung cancer. Multidetector computed-tomography (MDCT) scanners provide three-dimensional (3D) sub-millimeter-resolution X-ray CT images of the chest, positron emission tomography (PET) scanners give complementary molecular-imaging information, and new integrated PET/CT scanners combine the strengths of both modalities. State-of-the- art bronchoscopes provide 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. Nevertheless, the 5-year survival rate of lung-cancer patients remains under 15%, implying that these new tools have not yet been fully exploited. The objective now is to devise an IGI system that incorporates multimodal imaging techniques throughout the lung-cancer assessment process. In particular, the project is driven by the following hypothesis: A synergistic integration of multimodal 3D imaging and bronchoscopy, coupled with computer-based image processing, improves current techniques for the diagnosis and staging of lung cancer.
The specific aims of the project are as follows:
Aim 1 : Devise methods for multimodal procedure planning and image-guided bronchoscopy.
Aim 2 : Construct a fully integrated multimodal image-guided intervention system.
Aim 3 : Perform human studies to optimize and validate the complete system. If successful, the proposed multimodal imaging, biopsy, and treatment-planning techniques could enable correct diagnosis and staging of lung cancer preoperatively in virtually all patients. In addition, the proposed techniques would greatly facilitate other ongoing developments striving to localize early lung cancers and assist in the treatment of emphysema and other lung diseases. Finally, many of the proposed basic methods should be applicable to other areas, such as urology, gastroenterology, nephrology, gynecology, and neurology.
Lung cancer is the leading cause of cancer death worldwide, accounting for over 1.3 million deaths in 2008. The long-term goal of this project is to construct a multimodal image- guided intervention system for more effective lung-cancer diagnosis and staging.
|Byrnes, Patrick D; Higgins, William Evan (2018) Efficient Bronchoscopic Video Summarization. IEEE Trans Biomed Eng :|
|Zang, Xiaonan; Bascom, Rebecca; Gilbert, Christopher et al. (2016) Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation. IEEE Trans Biomed Eng 63:1426-39|
|Khare, Rahul; Bascom, Rebecca; Higgins, William E (2015) Hands-Free System for Bronchoscopy Planning and Guidance. IEEE Trans Biomed Eng 62:2794-811|
|Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W et al. (2015) Thoracic cavity definition for 3D PET/CT analysis and visualization. Comput Biol Med 62:222-38|
|Gibbs, Jason D; Graham, Michael W; Bascom, Rebecca et al. (2014) Optimal procedure planning and guidance system for peripheral bronchoscopy. IEEE Trans Biomed Eng 61:638-57|
|Merritt, Scott A; Khare, Rahul; Bascom, Rebecca et al. (2013) Interactive CT-video registration for the continuous guidance of bronchoscopy. IEEE Trans Med Imaging 32:1376-96|
|Graham, Michael W; Gibbs, Jason D; Higgins, William E (2012) Computer-based route-definition system for peripheral bronchoscopy. J Digit Imaging 25:307-17|
|Lu, Kongkuo; Taeprasartsit, Pinyo; Bascom, Rebecca et al. (2011) Automatic definition of the central-chest lymph-node stations. Int J Comput Assist Radiol Surg 6:539-55|
|Lu, Kongkuo; Higgins, William E (2011) Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med 41:780-9|