Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework

Ashrani Aizzuddin Abd Rahni, E. Lewis, K. Wells, J. Jones

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

2 Citations (Scopus)

Abstract

The continual improvement in spatial resolution of Nuclear Medicine (NM) scanners has made accurate compensation of patient motion increasingly important. A major source of corrupting motion in NM acquisition is due to respiration. Therefore a particle filter (PF) approach has been proposed as a powerful method for motion correction in NM. The probabilistic view of the system in the PF has an advantage in that it considers the complexity and uncertainties of respiratory motion. Tests using the XCAT phantom have previously shown the possibility of estimating unseen organ configurations using training data that only consist of a single respiratory cycle. This paper builds upon previous work in two ways: (i) this is the first evaluation of a PF framework using clinical 4D thoracic CT data; and, (ii) this implementation uses a kernel density estimation (KDE) representation for the transition model, thus taking advantage of the PF's ability to use a wider range of stochastic models. The results show some improvement with the use of a KDE-based transition model and indicates that the PF should be applicable to clinical data.

Original languageEnglish
Title of host publicationIEEE Nuclear Science Symposium Conference Record
Pages2928-2932
Number of pages5
DOIs
Publication statusPublished - 2012
Event2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011 - Valencia
Duration: 23 Oct 201129 Oct 2011

Other

Other2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011
CityValencia
Period23/10/1129/10/11

Fingerprint

nuclear medicine
Nuclear Medicine
filters
Spatial Analysis
Four-Dimensional Computed Tomography
respiration
organs
scanners
Uncertainty
acquisition
Respiration
education
estimating
Thorax
spatial resolution
cycles
evaluation
configurations

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

Cite this

Abd Rahni, A. A., Lewis, E., Wells, K., & Jones, J. (2012). Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework. In IEEE Nuclear Science Symposium Conference Record (pp. 2928-2932). [6152522] https://doi.org/10.1109/NSSMIC.2011.6152522

Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework. / Abd Rahni, Ashrani Aizzuddin; Lewis, E.; Wells, K.; Jones, J.

IEEE Nuclear Science Symposium Conference Record. 2012. p. 2928-2932 6152522.

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

Abd Rahni, AA, Lewis, E, Wells, K & Jones, J 2012, Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework. in IEEE Nuclear Science Symposium Conference Record., 6152522, pp. 2928-2932, 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011, Valencia, 23/10/11. https://doi.org/10.1109/NSSMIC.2011.6152522
Abd Rahni AA, Lewis E, Wells K, Jones J. Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework. In IEEE Nuclear Science Symposium Conference Record. 2012. p. 2928-2932. 6152522 https://doi.org/10.1109/NSSMIC.2011.6152522
Abd Rahni, Ashrani Aizzuddin ; Lewis, E. ; Wells, K. ; Jones, J. / Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework. IEEE Nuclear Science Symposium Conference Record. 2012. pp. 2928-2932
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