Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging

Smith, RL, Rahni, AA, Jones, J and Wells, K (2012) Recursive Bayesian estimation for respiratory motion correction in nuclear medicine imaging IEEE Nuclear Science Symposium Conference Record. pp. 2942-2945.

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Abstract

Respiratory motion correction degrades quantitatively and qualitatively Nuclear Medicine images. We propose that adaptive approaches are required to correct for the irregular breathing patterns often encountered in the clinical setting, which can be addressed within a Bayesian tracking formulation. This allows inference of the hidden organ configurations using only knowledge of an external observation such as a parametrized external surface. The flexible framework described provides a method to correct for organ motion whilst accommodating for irregular unseen respiratory patterns. In this work we utilize a Kalman filter and compare it with a Particle filter. A novel adaptive state transition model is also introduced to describe the evolution of organ configurations. The Kalman filter marginally outperforms the Particle filter, both approaches however offer an effective motion correction mechanism, correcting for motion with errors of around 1-3mm. We present results of simulated PET images derived from XCAT to demonstrate the efficacy of the approach. © 2012 IEEE.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Smith, RL Rahni, AA Jones, J Wells, K
Date :	2012
DOI :	10.1109/NSSMIC.2012.6551672
Additional Information :	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User :	Symplectic Elements
Date Deposited :	27 Aug 2014 13:40
Last Modified :	31 Oct 2017 16:31
URI:	http://epubs.surrey.ac.uk/id/eprint/804821

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