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dc.contributor.authorTsoumpas, Charalampos
dc.contributor.authorPolycarpou, Irene
dc.contributor.authorThielemans, Kris
dc.contributor.authorBuerger, Christian
dc.contributor.authorKing, Andrew P.
dc.contributor.authorSchaeffter, Tobias R.
dc.contributor.authorMarsden, Paul K.
dc.creatorTsoumpas, Charalampos
dc.date.accessioned2018-10-10T06:54:25Z
dc.date.available2018-10-10T06:54:25Z
dc.date.issued2013-03-21
dc.identifierSCOPUS_ID:84874870188
dc.identifier.issn00319155
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84874870188&origin=inward
dc.identifier.urihttps://repo.euc.ac.cy/handle/123456789/394
dc.description.abstractFollowing continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.
dc.relation.ispartofPhysics in Medicine and Biology
dc.titleThe effect of regularization in motion compensated PET image reconstruction: A realistic numerical 4D simulation study
elsevier.identifier.doi10.1088/0031-9155/58/6/1759
elsevier.identifier.eid2-s2.0-84874870188
elsevier.identifier.scopusidSCOPUS_ID:84874870188
elsevier.volume58
elsevier.issue.identifier6
elsevier.coverdate2013-03-21
elsevier.coverdisplaydate21 March 2013
elsevier.openaccess1
elsevier.openaccessflagtrue
elsevier.aggregationtypeJournal


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