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

doi:10.1098/rsta.2020.0208

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 language	English
Article number	20200208
Journal	Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Volume	379
Issue number	2204
Early online date	5 Jul 2021
DOIs	https://doi.org/10.1098/rsta.2020.0208
Publication status	Published - 23 Aug 2021

Bibliographical note

Funding Information:
Ethics. Patients gave written, informed consent to take part in the study and to undergo a PET/MR scan after the original PET/CT scan without any additional injection of a radionuclide tracer. The study was approved by National Research Ethics Service Committee at King’s College London (no. 15/LO/0978). Data accessibility. Reconstruction scripts and installation instructions for the specific versions of code used in this research can be found in our contributor’s GitHub page. This resource includes information of supported python versions and CI/CD testing. Authors’ contributions. R.B. developed PET and registration functionality, co-developed the PET MCIR reconstruction script, ran PET experiments and drafted part of the manuscript. E.Pas. developed the interoperability between SIRF and CIL, co-developed the PET and MR MCIR reconstruction scripts and ran the PET reconstructions on the STFC cluster. E.Pap. co-developed the PET MCIR reconstruction script. C.K. and J.M. developed the GRPE acquisition model, the MR MCIR reconstruction script, carried out the MR reconstruction and drafted this part of the manuscript. R.N. helped with the development of used MR sequences with CK. C.D. co-developed the PET MCIR reconstruction script, carried out PET experiments and drafted this part of the manuscript. JRM and B.E. implemented adjoint image resampling functionality. E.O. developed the core of the SIRF code base. C.d.C.L. aided with code infrastructure, example scripts, testing and continuous integration. A.G. developed code for handling patient orientation as well as aiding draft the PET and motion correction sections of the paper. M.J.E. aided with mathematical theory behind PDHG. K.T. provided general oversight of the project and software and drafted part of the manuscript. All authors read and approved the manuscript. Competing interests. Dr Neji is an employee of Siemens Healthcare. Funding. This work was funded by the UK EPSRC grants ‘Computational Collaborative Project in Synergistic PET/MR Reconstruction’ (CCP PETMR) EP/M022587/1 and its associated Software Flagship project EP/P022200/1; the ‘Computational Collaborative Project in Synergistic Reconstruction for Biomedical

Funding Information:
Imaging’ (CCP SyneRBI) EP/T026693/1; ‘A Reconstruction Toolkit for Multichannel CT’ EP/P02226X/1 and ‘Collaborative Computational Project in tomographic imaging’ (CCPi) EP/M022498/1 and EP/T026677/1; ‘PET++: Improving Localization, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomized Optimization’ EP/S026045/1. This work made use of computational support by CoSeC, the Computational Science Centre for Research Communities, through CCP SyneRBI and CCPi. Acknowledgements. We thank Simon Arridge, David Atkinson, Julian Matthews, Andrew Reader, Steven Sourbron, Charalampos Tsoumpas and Martyn Winn for co-organizing the CCP PETMR/SyneRBI network, its community for feedback, and Jakob S. Jørgensen and other members of CCPi for interactions on the software and algorithms. Computing resources were provided by STFC Scientific Computing Department’s SCARF cluster and the STFC Cloud.

Publisher Copyright:
© 2021 The Author(s).

Keywords

MR
Motion
PET
SIRF
correction
estimation

ASJC Scopus subject areas

General Engineering
General Physics and Astronomy
General Mathematics

Access to Document

10.1098/rsta.2020.0208

SIRF_MC_special_issueAccepted author manuscript, 2.19 MBLicence: CC BY

Cite this

Brown, R., Kolbitsch, C., Delplancke, C., Papoutsellis, E., Mayer, J., Ovtchinnikov, E., Pasca, E., Neji, R., da Costa-Luis, C., Gillman, A. G., Ehrhardt, M. J., McClelland, J. R., Eiben, B., & Thielemans, K. (2021). Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 379(2204), Article 20200208. https://doi.org/10.1098/rsta.2020.0208

Brown, R, Kolbitsch, C, Delplancke, C, Papoutsellis, E, Mayer, J, Ovtchinnikov, E, Pasca, E, Neji, R, da Costa-Luis, C, Gillman, AG, Ehrhardt, MJ, McClelland, JR, Eiben, B & Thielemans, K 2021, 'Motion estimation and correction for simultaneous PET/MR using SIRF and CIL', Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, vol. 379, no. 2204, 20200208. https://doi.org/10.1098/rsta.2020.0208

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title = "Motion estimation and correction for simultaneous PET/MR using SIRF and CIL",

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'.",

keywords = "MR, Motion, PET, SIRF, correction, estimation",

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

note = "Funding Information: Ethics. Patients gave written, informed consent to take part in the study and to undergo a PET/MR scan after the original PET/CT scan without any additional injection of a radionuclide tracer. The study was approved by National Research Ethics Service Committee at King{\textquoteright}s College London (no. 15/LO/0978). Data accessibility. Reconstruction scripts and installation instructions for the specific versions of code used in this research can be found in our contributor{\textquoteright}s GitHub page. This resource includes information of supported python versions and CI/CD testing. Authors{\textquoteright} contributions. R.B. developed PET and registration functionality, co-developed the PET MCIR reconstruction script, ran PET experiments and drafted part of the manuscript. E.Pas. developed the interoperability between SIRF and CIL, co-developed the PET and MR MCIR reconstruction scripts and ran the PET reconstructions on the STFC cluster. E.Pap. co-developed the PET MCIR reconstruction script. C.K. and J.M. developed the GRPE acquisition model, the MR MCIR reconstruction script, carried out the MR reconstruction and drafted this part of the manuscript. R.N. helped with the development of used MR sequences with CK. C.D. co-developed the PET MCIR reconstruction script, carried out PET experiments and drafted this part of the manuscript. JRM and B.E. implemented adjoint image resampling functionality. E.O. developed the core of the SIRF code base. C.d.C.L. aided with code infrastructure, example scripts, testing and continuous integration. A.G. developed code for handling patient orientation as well as aiding draft the PET and motion correction sections of the paper. M.J.E. aided with mathematical theory behind PDHG. K.T. provided general oversight of the project and software and drafted part of the manuscript. All authors read and approved the manuscript. Competing interests. Dr Neji is an employee of Siemens Healthcare. Funding. This work was funded by the UK EPSRC grants {\textquoteleft}Computational Collaborative Project in Synergistic PET/MR Reconstruction{\textquoteright} (CCP PETMR) EP/M022587/1 and its associated Software Flagship project EP/P022200/1; the {\textquoteleft}Computational Collaborative Project in Synergistic Reconstruction for Biomedical Funding Information: Imaging{\textquoteright} (CCP SyneRBI) EP/T026693/1; {\textquoteleft}A Reconstruction Toolkit for Multichannel CT{\textquoteright} EP/P02226X/1 and {\textquoteleft}Collaborative Computational Project in tomographic imaging{\textquoteright} (CCPi) EP/M022498/1 and EP/T026677/1; {\textquoteleft}PET++: Improving Localization, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomized Optimization{\textquoteright} EP/S026045/1. This work made use of computational support by CoSeC, the Computational Science Centre for Research Communities, through CCP SyneRBI and CCPi. Acknowledgements. We thank Simon Arridge, David Atkinson, Julian Matthews, Andrew Reader, Steven Sourbron, Charalampos Tsoumpas and Martyn Winn for co-organizing the CCP PETMR/SyneRBI network, its community for feedback, and Jakob S. J{\o}rgensen and other members of CCPi for interactions on the software and algorithms. Computing resources were provided by STFC Scientific Computing Department{\textquoteright}s SCARF cluster and the STFC Cloud. Publisher Copyright: {\textcopyright} 2021 The Author(s).",

year = "2021",

month = aug,

day = "23",

doi = "10.1098/rsta.2020.0208",

language = "English",

volume = "379",

journal = "Philosophical transactions. Series A, Mathematical, physical, and engineering sciences",

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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

N1 - Funding Information: Ethics. Patients gave written, informed consent to take part in the study and to undergo a PET/MR scan after the original PET/CT scan without any additional injection of a radionuclide tracer. The study was approved by National Research Ethics Service Committee at King’s College London (no. 15/LO/0978). Data accessibility. Reconstruction scripts and installation instructions for the specific versions of code used in this research can be found in our contributor’s GitHub page. This resource includes information of supported python versions and CI/CD testing. Authors’ contributions. R.B. developed PET and registration functionality, co-developed the PET MCIR reconstruction script, ran PET experiments and drafted part of the manuscript. E.Pas. developed the interoperability between SIRF and CIL, co-developed the PET and MR MCIR reconstruction scripts and ran the PET reconstructions on the STFC cluster. E.Pap. co-developed the PET MCIR reconstruction script. C.K. and J.M. developed the GRPE acquisition model, the MR MCIR reconstruction script, carried out the MR reconstruction and drafted this part of the manuscript. R.N. helped with the development of used MR sequences with CK. C.D. co-developed the PET MCIR reconstruction script, carried out PET experiments and drafted this part of the manuscript. JRM and B.E. implemented adjoint image resampling functionality. E.O. developed the core of the SIRF code base. C.d.C.L. aided with code infrastructure, example scripts, testing and continuous integration. A.G. developed code for handling patient orientation as well as aiding draft the PET and motion correction sections of the paper. M.J.E. aided with mathematical theory behind PDHG. K.T. provided general oversight of the project and software and drafted part of the manuscript. All authors read and approved the manuscript. Competing interests. Dr Neji is an employee of Siemens Healthcare. Funding. This work was funded by the UK EPSRC grants ‘Computational Collaborative Project in Synergistic PET/MR Reconstruction’ (CCP PETMR) EP/M022587/1 and its associated Software Flagship project EP/P022200/1; the ‘Computational Collaborative Project in Synergistic Reconstruction for Biomedical Funding Information: Imaging’ (CCP SyneRBI) EP/T026693/1; ‘A Reconstruction Toolkit for Multichannel CT’ EP/P02226X/1 and ‘Collaborative Computational Project in tomographic imaging’ (CCPi) EP/M022498/1 and EP/T026677/1; ‘PET++: Improving Localization, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomized Optimization’ EP/S026045/1. This work made use of computational support by CoSeC, the Computational Science Centre for Research Communities, through CCP SyneRBI and CCPi. Acknowledgements. We thank Simon Arridge, David Atkinson, Julian Matthews, Andrew Reader, Steven Sourbron, Charalampos Tsoumpas and Martyn Winn for co-organizing the CCP PETMR/SyneRBI network, its community for feedback, and Jakob S. Jørgensen and other members of CCPi for interactions on the software and algorithms. Computing resources were provided by STFC Scientific Computing Department’s SCARF cluster and the STFC Cloud. Publisher Copyright: © 2021 The Author(s).

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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'.

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ER -

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

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Bibliographical note

Keywords

ASJC Scopus subject areas

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