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Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework

Rahni, AAA, Rahni, AAA, Lewis, E, Wells, K, Wells, K and Jones, J (2012) Respiratory motion estimation in nuclear medicine imaging using a kernel model-based particle filter framework

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Abstract

The continual improvement in spatial resolution of Nuclear Medicine (NM) scanners has made accurate compensation of patient motion increasingly important. A major source of corrupting motion in NM acquisition is due to respiration. Therefore a particle filter (PF) approach has been proposed as a powerful method for motion correction in NM. The probabilistic view of the system in the PF has an advantage in that it considers the complexity and uncertainties of respiratory motion. Tests using the XCAT phantom have previously shown the possibility of estimating unseen organ configurations using training data that only consist of a single respiratory cycle. This paper builds upon previous work in two ways: (i) this is the first evaluation of a PF framework using clinical 4D thoracic CT data; and, (ii) this implementation uses a kernel density estimation (KDE) representation for the transition model, thus taking advantage of the PF's ability to use a wider range of stochastic models. The results show some improvement with the use of a KDE-based transition model and indicates that the PF should be applicable to clinical data. © 2011 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Rahni, AAAUNSPECIFIEDUNSPECIFIED
Rahni, AAAUNSPECIFIEDUNSPECIFIED
Lewis, EUNSPECIFIEDUNSPECIFIED
Wells, KUNSPECIFIEDUNSPECIFIED
Wells, KUNSPECIFIEDUNSPECIFIED
Jones, JUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : 10.1109/NSSMIC.2011.6152522
Additional Information : © 2011 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 : 24 Jan 2014 10:22
Last Modified : 09 Jun 2014 13:50
URI: http://epubs.surrey.ac.uk/id/eprint/804798

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