@article{discovery10131780,
         journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
            year = {2021},
           title = {Motion estimation and correction for simultaneous PET/MR using SIRF and CIL},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {379},
          number = {2204},
           month = {August},
        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'.},
          author = {Brown, R and Kolbitsch, C and Delplancke, C and Papoutsellis, E and Mayer, J and Ovtchinnikov, E and Pasca, E and Neji, R and da Costa-Luis, C and Gillman, AG and Ehrhardt, MJ and McClelland, JR and Eiben, B and Thielemans, K},
             url = {https://doi.org/10.1098/rsta.2020.0208},
        keywords = {MR, Motion, PET, SIRF, correction, estimation}
}