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Proceedings Paper

Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model
Author(s): Ashrani Aizzuddin Abd. Rahni; Emma Lewis; Kevin Wells
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Paper 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.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866935 (13 March 2013); doi: 10.1117/12.2006878
Show Author Affiliations
Ashrani Aizzuddin Abd. Rahni, Univ. Kebangsaan Malaysia (Malaysia)
Univ. of Surrey (United Kingdom)
Emma Lewis, Univ. of Surrey (United Kingdom)
Kevin Wells, Univ. of Surrey (United Kingdom)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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