Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

Richard Brown, Christoph Kolbitsch, Claire Delplancke, Evangelos Papoutsellis, Johannes Mayer, Evgueni Ovtchinnikov, Edoardo Pasca, Radhouene Neji, Casper da Costa-Luis, Ashley G Gillman, Matthias J Ehrhardt, Jamie R McClelland, Bjoern Eiben, Kris Thielemans

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Abstract

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Original languageEnglish
Pages (from-to)20200208
JournalPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
Volume379
Issue number2204
DOIs
Publication statusPublished - 23 Aug 2021

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