
Proceedings Paper
Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition modelFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
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)
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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