Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model

Ashrani Aizzuddin Abd Rahni, Emma Lewis, Kevin Wells

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

Abstract

Compensation for respiratory motion has been identified as a crucial factor in achieving high resolution Nuclear Medicine (NM) imaging. Many motion correction approaches have been studied and they are seen to have advantages over simpler approaches such as respiratory gating. However, all motion correction approaches rely on an assumption or estimation of respiratory motion. This paper builds upon previous work in recursive Bayesian estimation of respiratory motion assuming a stereo camera observation of the motion of the external torso surface. This paper compares the performance of a modified autoregressive transition model against the previously presented linear transition model used when estimating motion within a 4D dataset generated from the XCAT phantom.

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8669
DOIs
Publication statusPublished - 2013
EventMedical Imaging 2013: Image Processing - Lake Buena Vista, FL
Duration: 10 Feb 201312 Feb 2013

Other

OtherMedical Imaging 2013: Image Processing
CityLake Buena Vista, FL
Period10/2/1312/2/13

Fingerprint

Nuclear medicine
Cameras
Imaging techniques
torso
Torso
nuclear medicine
Nuclear Medicine
Linear Models
estimating
cameras
Observation
high resolution
Compensation and Redress

Keywords

  • Modified autoregression
  • Nuclear medicine
  • Recursive Bayesian estimation
  • Respiratory motion

ASJC Scopus subject areas

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

Cite this

Abd Rahni, A. A., Lewis, E., & Wells, K. (2013). Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 8669). [866935] https://doi.org/10.1117/12.2006878

Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model. / Abd Rahni, Ashrani Aizzuddin; Lewis, Emma; Wells, Kevin.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8669 2013. 866935.

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

Abd Rahni, AA, Lewis, E & Wells, K 2013, Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 8669, 866935, Medical Imaging 2013: Image Processing, Lake Buena Vista, FL, 10/2/13. https://doi.org/10.1117/12.2006878
Abd Rahni AA, Lewis E, Wells K. Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8669. 2013. 866935 https://doi.org/10.1117/12.2006878
Abd Rahni, Ashrani Aizzuddin ; Lewis, Emma ; Wells, Kevin. / Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8669 2013.
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