Multimodality Image-Guided Lung Intervention Systems

Kongkuo Lu, Tiancheng He, Sheng Xu, Miguel Valdivia Alvarado, Zhong Xue

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Medical imaging provides internal anatomical information of the human body to facilitate minimally invasive interventional procedures. Ideally, image-guided intervention requires both the device tracking and imaging to be performed in real time, and recent development of medical imaging and device-tracking techniques makes it possible to visualize both devices and patients' anatomy during intervention. However, when real-time imaging is not applicable, patient motion tracking and image registration or motion compensation play important roles in generating more realistic image roadmaps for the guidance. Following discussion of the traditional techniques for multimodality image-guided intervention, this chapter focuses on how to integrate device tracking with multimodality imaging and introduces data fusion and dynamic image guidance in the context of image-guided bronchoscopy and percutaneous lung cancer intervention. It is expected that with advanced sensors and dynamic image modeling, more accurate real-time estimation about the interventional roadmap and more efficient, accurate, and safer intervention procedures can be achieved.

Original languageEnglish (US)
Title of host publicationCancer Theranostics
PublisherElsevier
Pages161-186
Number of pages26
ISBN (Print)9780124077225
DOIs
StatePublished - Mar 2014

Keywords

  • Bronchoscopy
  • Computed tomography
  • Computed tomography fluoroscopy
  • Fluoroscopy
  • Image fusion
  • Image-guided intervention
  • Magnetic resonance imaging
  • Motion compensation
  • Percutaneous lung intervention
  • Ultrasound

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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