Accelerated Regularised List-Mode PET Reconstruction Using Subset Relaxation

Matthew G. Spangler-Bickell, Timothy Deller, Floris Jansen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

PET image reconstruction using regularisation algorithms can provide good image quality and ensure convergence with suitable parameter selections, however they usually require many iterations to do so. A list-mode form of the BSREM regularised algorithm for PET image reconstruction is presented, with an acceleration technique whereby the number of list-mode events used in each subset is increased with increasing iteration number. This allows for quick convergence in early iterations, and avoids noise propagation from "small" subsets as well as limit cycles in later iterations.

Original languageEnglish
Title of host publication2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141640
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 - Manchester, United Kingdom
Duration: Oct 26 2019Nov 2 2019

Publication series

Name2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Country/TerritoryUnited Kingdom
CityManchester
Period10/26/1911/2/19

Funding

Manuscript received on 13 December 2019. This work was supported by a fellowship from GE Healthcare.

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