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Adaptive adjustment of the number of subsets during iterative image reconstruction

Thielemans, K; Arridge, S; (2016) Adaptive adjustment of the number of subsets during iterative image reconstruction. In: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE Green open access

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Abstract

A common strategy to speed-up image reconstruction in tomography is to use subsets, i.e. only part of the data is used to compute the update, as for instance in the OSEM algorithm. However, most subset algorithms do not convergence or have a limit cycle. Different strategies to solve this problem exist, for instance using relaxation. The conceptually easiest mechanism is to reduce the number of subsets gradually during iterations. However, the optimal point to reduce the number of subsets is usually depends on many factors such as initialisation, the object itself, amount of noise etc. In this paper, we propose a simple scheme to automatically compute if the number of subsets is too large (or too small) and adjust the size of the data to consider in the next update automatically. The scheme is based on idea of computing two image updates corresponding to different parts of the data. A comparison of these updates then allows to see if the updates were sufficiently consistent or not. We illustrate this idea using 2 different subset algorithms: OSEM and OSSPS.

Type: Proceedings paper
Title: Adaptive adjustment of the number of subsets during iterative image reconstruction
Event: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference: 22nd International Symposium on Room-Temperature Semiconductor X-Ray and Gamma-ray Detectors
Location: San Diego, California, USA
Dates: 31 October 2015 - 07 November 2015
ISBN-13: 9781467398626
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NSSMIC.2015.7582225
Publisher version: http://ieeexplore.ieee.org/document/7582225/
Language: English
Additional information: Copyright © 2015 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.
Keywords: Image reconstruction, Tomography, Sensitivity, Convergence, Limit-cycles, Computational modeling, image updates, iterative image reconstruction, OSEM subset algorithm, OSSPS subset algorithm
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Metabolism and Experi Therapeutics
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1529609
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