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

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 grad...

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Published in:	Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences physical, and engineering sciences, 2021-08, Vol.379 (2204), p.20200208-20200208
Main Authors:	Brown, Richard, Kolbitsch, Christoph, Delplancke, Claire, Papoutsellis, Evangelos, Mayer, Johannes, Ovtchinnikov, Evgueni, Pasca, Edoardo, Neji, Radhouene, da Costa-Luis, Casper, Gillman, Ashley G, Ehrhardt, Matthias J, McClelland, Jamie R, Eiben, Bjoern, Thielemans, Kris
Format:	Article
Language:	eng
Subjects:	Algorithms Humans Image Interpretation, Computer-Assisted - statistics & numerical data Imaging, Three-Dimensional - statistics & numerical data Magnetic Resonance Imaging - statistics & numerical data Motion Multimodal Imaging - statistics & numerical data Positron-Emission Tomography - statistics & numerical data Respiration Software
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Summary:	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'.
ISSN:	1364-503X 1471-2962