Construction of a multimodal CT-video chest model

Patrick D. Byrnes, William E. Higgins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscopea'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 patienta's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patienta'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 videoa'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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
PublisherSPIE
ISBN (Print)9780819498298
DOIs
StatePublished - Jan 1 2014
EventMedical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, CA, United States
Duration: Feb 18 2014Feb 20 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9036
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego, CA
Period2/18/142/20/14

Fingerprint

chest
Multidetector computed tomography
Thorax
tomography
Tomography
physicians
data structures
detectors
Bronchoscopy
lungs
asthma
Data structures
Physicians
Bronchoscopes
video data
condensing
Information Storage and Retrieval
navigation
linkages
health

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Byrnes, P. D., & Higgins, W. E. (2014). Construction of a multimodal CT-video chest model. In Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling [903607] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9036). SPIE. https://doi.org/10.1117/12.2041609
Byrnes, Patrick D. ; Higgins, William E. / Construction of a multimodal CT-video chest model. Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE, 2014. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{9b4e8f687b42475eb44798f93327f789,
title = "Construction of a multimodal CT-video chest model",
abstract = "Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscopea'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 patienta's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patienta'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 videoa'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.",
author = "Byrnes, {Patrick D.} and Higgins, {William E.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1117/12.2041609",
language = "English (US)",
isbn = "9780819498298",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
address = "United States",

}

Byrnes, PD & Higgins, WE 2014, Construction of a multimodal CT-video chest model. in Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling., 903607, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9036, SPIE, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, United States, 2/18/14. https://doi.org/10.1117/12.2041609

Construction of a multimodal CT-video chest model. / Byrnes, Patrick D.; Higgins, William E.

Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE, 2014. 903607 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9036).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Construction of a multimodal CT-video chest model

AU - Byrnes, Patrick D.

AU - Higgins, William E.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscopea'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 patienta's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patienta'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 videoa'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.

AB - Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscopea'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 patienta's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patienta'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 videoa'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.

UR - http://www.scopus.com/inward/record.url?scp=84902185864&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902185864&partnerID=8YFLogxK

U2 - 10.1117/12.2041609

DO - 10.1117/12.2041609

M3 - Conference contribution

AN - SCOPUS:84902185864

SN - 9780819498298

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2014

PB - SPIE

ER -

Byrnes PD, Higgins WE. Construction of a multimodal CT-video chest model. In Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE. 2014. 903607. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2041609