Total variation regularization in measurement and image space for PET reconstruction

M Burger, J. D. Müller, E Papoutsellis, C B Schönlieb

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


© 2014 IOP Publishing Ltd. The aim of this paper is to test and analyse a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both the image space and the projection space. We formulate our variational problem considering both total variation penalty terms on the image and on an idealized sinogram to be reconstructed from a given Poisson distributed noisy sinogram. We prove existence, uniqueness and stability results for the proposed model and provide some analytical insight into the structures favoured by joint regularization. For the numerical solution of the corresponding discretized problem we employ the split Bregman algorithm and extensively test the approach in comparison to standard total variation regularization on the image. The numerical results show that an additional penalty on the sinogram performs better on reconstructing images with thin structures.
Original languageEnglish (US)
Pages (from-to)105003
JournalInverse Problems
Issue number10
StatePublished - Sep 18 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-I1-007-43
Acknowledgements: This work has been financially supported by the King Abdullah University of Science and Technology (KAUST) Award No. KUK-I1-007-43, the EPSRC first grant EP/J009539/1, the Royal Society International Exchange Award Nr. IE110314, and the German Science Foundation (DFG) through the Collaborative Research Centre SFB 656 subproject B2 and Cells-in-Motion Cluster of Excellence (EXC 1003 CiM), University of Munster.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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