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

Ashrani Aizzuddin Abd. Rahni | Zendy; Emma Lewis | Zendy; Kevin Wells | Zendy

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

Author(s) -

Ashrani Aizzuddin Abd. Rahni,

Emma Lewis,

Kevin Wells

Publication year - 2013

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2006878

Subject(s) - autoregressive model , torso , motion estimation , computer science , bayesian probability , motion (physics) , artificial intelligence , computer vision , imaging phantom , motion compensation , bayes estimator , bayesian inference , algorithm , mathematics , statistics , physics , medicine , optics , anatomy

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

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