Motion estimation and correction for simultaneous PET/MR using SIRF and CIL-Reference-Cited by-同舟云学术

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

Published:2021-07-05 Issue:2204 Volume:379 Page:20200208
ISSN:1364-503X
Container-title:Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
language:en
Short-container-title:Phil. Trans. R. Soc. A.

Author:

Brown Richard¹²^ORCID,Kolbitsch Christoph²³^ORCID,Delplancke Claire⁴^ORCID,Papoutsellis Evangelos⁵⁶^ORCID,Mayer Johannes³^ORCID,Ovtchinnikov Evgueni⁵,Pasca Edoardo⁵^ORCID,Neji Radhouene²⁷,da Costa-Luis Casper²^ORCID,Gillman Ashley G.⁸^ORCID,Ehrhardt Matthias J.⁴⁹^ORCID,McClelland Jamie R.¹⁰¹¹^ORCID,Eiben Bjoern¹⁰¹¹^ORCID,Thielemans Kris¹¹¹^ORCID

Affiliation:

1. Institute of Nuclear Medicine, University College London, London, UK

2. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

3. Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany

4. Department of Mathematical Sciences, University of Bath, Bath, UK

5. Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK

6. Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK

7. MR Research Collaborations, Siemens Healthcare, Frimley, UK

8. Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia

9. Institute for Mathematical Innovation, University of Bath, UK

10. Centre for Medical Image Computing, University College London, UK

11. Department of Medical Physics and Biomedical Engineering, University College London, UK

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’.

Funder

Engineering and Physical Sciences Research Council

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

Link

https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0208

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