Estimating dynamic lung images from high-dimension chest surface motion using 4D statistical model

Tiancheng He, Zhong Xue, Nam Yu, Paige L. Nitsch, Bin S. Teh, Stephen T. Wong

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

1 Scopus citations

Abstract

Computed Tomography (CT) has been widely used in image-guided procedures such as intervention and radiotherapy of lung cancer. However, due to poor reproducibility of breath holding or respiratory cycles, discrepancies between static images and patient's current lung shape and tumor location could potentially reduce the accuracy for image guidance. Current methods are either using multiple intra-procedural scans or monitoring respiratory motion with tracking sensors. Although intra-procedural scanning provides more accurate information, it increases the radiation dose and still only provides snapshots of patient's chest. Tracking-based breath monitoring techniques can effectively detect respiratory phases but have not yet provided accurate tumor shape and location due to low dimensional signals. Therefore, estimating the lung motion and generating dynamic CT images from real-time captured high-dimensional sensor signals acts as a key component for image-guided procedures. This paper applies a principal component analysis (PCA)-based statistical model to establish the relationship between lung motion and chest surface motion from training samples, on a template space, and then uses this model to estimate dynamic images for a new patient from the chest surface motion. Qualitative and quantitative results showed that the proposed high-dimensional estimation algorithm yielded more accurate 4D-CT compared to fiducial marker-based estimation.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer-Verlag
Pages138-145
Number of pages8
EditionPART 2
ISBN (Print)9783319104690
DOIs
StatePublished - 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8674 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period9/14/149/18/14

Keywords

  • high-dimensional respiratory motion estimation
  • statistical model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'Estimating dynamic lung images from high-dimension chest surface motion using 4D statistical model'. Together they form a unique fingerprint.

Cite this