TY - JOUR
T1 - Motion estimation and correction for simultaneous PET/MR using SIRF and CIL
AU - Brown, Richard
AU - Kolbitsch, Christoph
AU - Delplancke, Claire
AU - Papoutsellis, Evangelos
AU - Mayer, Johannes
AU - Ovtchinnikov, Evgueni
AU - Pasca, Edoardo
AU - Neji, Radhouene
AU - da Costa-Luis, Casper
AU - Gillman, Ashley G
AU - Ehrhardt, Matthias J
AU - McClelland, Jamie R
AU - Eiben, Bjoern
AU - Thielemans, Kris
PY - 2021/8/23
Y1 - 2021/8/23
N2 - 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'.
AB - 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'.
U2 - 10.1098/rsta.2020.0208
DO - 10.1098/rsta.2020.0208
M3 - Article
C2 - 34218674
SN - 1364-503X
VL - 379
SP - 20200208
JO - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
JF - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
IS - 2204
ER -