Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging

Rhodri L. Smith, Ashrani Aizzuddin Abd Rahni, John Jones, Kevin Wells

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

5 Citations (Scopus)

Abstract

Respiratory motion correction degrades quantitatively and qualitatively Nuclear Medicine images. We propose that adaptive approaches are required to correct for the irregular breathing patterns often encountered in the clinical setting, which can be addressed within a Bayesian tracking formulation. This allows inference of the hidden organ configurations using only knowledge of an external observation such as a parametrized external surface. The flexible framework described provides a method to correct for organ motion whilst accommodating for irregular unseen respiratory patterns. In this work we utilize a Kalman filter and compare it with a Particle filter. A novel adaptive state transition model is also introduced to describe the evolution of organ configurations. The Kalman filter marginally outperforms the Particle filter, both approaches however offer an effective motion correction mechanism, correcting for motion with errors of around 1-3mm. We present results of simulated PET images derived from XCAT to demonstrate the efficacy of the approach.

Original languageEnglish
Title of host publicationIEEE Nuclear Science Symposium Conference Record
Pages2942-2945
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012 - Anaheim, CA
Duration: 29 Oct 20123 Nov 2012

Other

Other2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
CityAnaheim, CA
Period29/10/123/11/12

Fingerprint

nuclear medicine
Nuclear Medicine
organs
Kalman filters
filters
breathing
configurations
inference
Respiration
Observation
formulations

Keywords

  • adaptive
  • recursive Bayesian estimation
  • Respiratory motion correction

ASJC Scopus subject areas

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

Cite this

Smith, R. L., Abd Rahni, A. A., Jones, J., & Wells, K. (2012). Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging. In IEEE Nuclear Science Symposium Conference Record (pp. 2942-2945). [6551672] https://doi.org/10.1109/NSSMIC.2012.6551672

Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging. / Smith, Rhodri L.; Abd Rahni, Ashrani Aizzuddin; Jones, John; Wells, Kevin.

IEEE Nuclear Science Symposium Conference Record. 2012. p. 2942-2945 6551672.

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

Smith, RL, Abd Rahni, AA, Jones, J & Wells, K 2012, Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging. in IEEE Nuclear Science Symposium Conference Record., 6551672, pp. 2942-2945, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012, Anaheim, CA, 29/10/12. https://doi.org/10.1109/NSSMIC.2012.6551672
Smith RL, Abd Rahni AA, Jones J, Wells K. Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging. In IEEE Nuclear Science Symposium Conference Record. 2012. p. 2942-2945. 6551672 https://doi.org/10.1109/NSSMIC.2012.6551672
Smith, Rhodri L. ; Abd Rahni, Ashrani Aizzuddin ; Jones, John ; Wells, Kevin. / Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging. IEEE Nuclear Science Symposium Conference Record. 2012. pp. 2942-2945
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