Interactive CT-Video Registration for the Continuous Guidance of Bronchoscopy

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 p...

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Veröffentlicht in:IEEE transactions on medical imaging 2013-08, Vol.32 (8), p.1376-1396
Hauptverfasser: Merritt, Scott A., Khare, Rahul, Bascom, Rebecca, Higgins, William E.
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Khare, Rahul
Bascom, Rebecca
Higgins, William E.
description 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.
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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. 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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. 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Khare, Rahul ; Bascom, Rebecca ; Higgins, William E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c482t-645da4f6cb9b0149b4f044daf2d18f3415047418f95ef376267c0343ea4c7df03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Bronchoscopy</topic><topic>Bronchoscopy - methods</topic><topic>Cameras</topic><topic>Computed tomography</topic><topic>endoscopy</topic><topic>Female</topic><topic>Humans</topic><topic>image alignment</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>image-based rendering</topic><topic>lung</topic><topic>Lung - anatomy &amp; histology</topic><topic>Lung - diagnostic imaging</topic><topic>Lung - pathology</topic><topic>lung cancer</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Lung Neoplasms - pathology</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Navigation</topic><topic>Real-time systems</topic><topic>registration</topic><topic>Streaming media</topic><topic>Surgery, Computer-Assisted - methods</topic><topic>surgical guidance/navigation</topic><topic>Thoracic Surgery, Video-Assisted - methods</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>virtual bronchoscopy</topic><topic>virtual endoscopy</topic><topic>virtual/augmented reality</topic><topic>X-ray imaging and computed tomography</topic><toplevel>online_resources</toplevel><creatorcontrib>Merritt, Scott A.</creatorcontrib><creatorcontrib>Khare, Rahul</creatorcontrib><creatorcontrib>Bascom, Rebecca</creatorcontrib><creatorcontrib>Higgins, William E.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEL</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Merritt, Scott A.</au><au>Khare, Rahul</au><au>Bascom, Rebecca</au><au>Higgins, William E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Interactive CT-Video Registration for the Continuous Guidance of Bronchoscopy</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2013-08-01</date><risdate>2013</risdate><volume>32</volume><issue>8</issue><spage>1376</spage><epage>1396</epage><pages>1376-1396</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>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.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>23508260</pmid><doi>10.1109/TMI.2013.2252361</doi><tpages>21</tpages><oa>free_for_read</oa></addata></record>
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subjects Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Bronchoscopy
Bronchoscopy - methods
Cameras
Computed tomography
endoscopy
Female
Humans
image alignment
Image Processing, Computer-Assisted - methods
image-based rendering
lung
Lung - anatomy & histology
Lung - diagnostic imaging
Lung - pathology
lung cancer
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - pathology
Male
Middle Aged
Navigation
Real-time systems
registration
Streaming media
Surgery, Computer-Assisted - methods
surgical guidance/navigation
Thoracic Surgery, Video-Assisted - methods
Tomography, X-Ray Computed - methods
virtual bronchoscopy
virtual endoscopy
virtual/augmented reality
X-ray imaging and computed tomography
title Interactive CT-Video Registration for the Continuous Guidance of Bronchoscopy
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