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

Abd Rahni, AA, Lewis, E and Wells, K (2013) Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 8669.

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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. © 2013 SPIE.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Abd Rahni, AAUNSPECIFIEDUNSPECIFIED
Lewis, EUNSPECIFIEDUNSPECIFIED
Wells, KUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : 10.1117/12.2006878
Additional Information : Copyright 2013 Proc. SPIE 8669, Medical Imaging 2013: Image Processing. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Ashrani Aizzuddin Abd. Rahni, Emma Lewis and Kevin Wells, "Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866935 (2013); doi:10.1117/12.2006878
Depositing User : Symplectic Elements
Date Deposited : 23 Jan 2014 15:15
Last Modified : 26 Sep 2014 13:33
URI: http://epubs.surrey.ac.uk/id/eprint/804795

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