UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Iterative PET Image Reconstruction using Adaptive Adjustment of Subset Size and Random Subset Sampling

Twyman, R; Arridge, S; Hutton, BF; Emond, EC; Brusaferri, L; Ahn, S; Thielemans, K; (2020) Iterative PET Image Reconstruction using Adaptive Adjustment of Subset Size and Random Subset Sampling. In: Proceedings of the 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE: Manchester, UK. Green open access

[thumbnail of RT_MIC_Conference_Record_2019_No_CR.pdf]
Preview
Text
RT_MIC_Conference_Record_2019_No_CR.pdf - Accepted Version

Download (790kB) | Preview

Abstract

Statistical PET image reconstruction methods are often accelerated by the use of a subset of available projections at each iteration. It is known that many subset algorithms, such as ordered subset expectation maximisation, will not converge to a single solution but to a limit cycle. Reconstruction methods exist to relax the update step sizes of subset algorithms to obtain convergence, however, this introduces additional parameters that may result in extended reconstruction times. Another approach is to gradually decrease the number of subsets to reduce the effect of the limit cycle at later iterations, but the optimal iteration numbers for these reductions may be data dependent. We propose an automatic method to increase subset sizes so a reconstruction can take advantage of the acceleration provided by small subset sizes during early iterations, while at later iterations reducing the effects of the limit cycle behaviour providing estimates closer to the maximum a posteriori solution. At each iteration, two image updates are computed from a common estimate using two disjoint subsets. The divergence of the two update vectors is measured and, if too great, subset sizes are increased in future iterations. We show results for both sinogram and list mode data using various subset selection methodologies.

Type: Proceedings paper
Title: Iterative PET Image Reconstruction using Adaptive Adjustment of Subset Size and Random Subset Sampling
Event: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
ISBN-13: 9781728141640
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NSS/MIC42101.2019.9059799
Publisher version: https://doi.org/10.1109/NSS/MIC42101.2019.9059799
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Image reconstruction, Linear programming, Acceleration, Computational efficiency, Size measurement, Tomography, Limit-cycles
UCL classification: UCL
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 > Department of Imaging
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 Chemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10096584
Downloads since deposit
79Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item