Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging
Pawel J. Markiewicz,
Julian C. Matthews,
John Ashburner,
David M. Cash,
David L. Thomas,
Enrico De Vita,
Anna Barnes,
M. Jorge Cardoso,
Marc Modat,
Richard Brown,
Kris Thielemans,
Casper da Costa-Luis,
Isadora Lopes Alves,
Juan Domingo Gispert,
Mark E. Schmidt,
Paul Marsden,
Alexander Hammers,
Sebastien Ourselin,
Frederik Barkhof
Affiliations
Pawel J. Markiewicz
Corresponding author.; Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Julian C. Matthews
Division of Neuroscience & Experimental Psychology, University of Manchester, UK
John Ashburner
Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK
David M. Cash
Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
David L. Thomas
Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
Enrico De Vita
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Anna Barnes
Institute of Nuclear Medicine, University College London, London, UK
M. Jorge Cardoso
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Marc Modat
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Richard Brown
Institute of Nuclear Medicine, University College London, London, UK
Kris Thielemans
Institute of Nuclear Medicine, University College London, London, UK
Casper da Costa-Luis
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK; Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK
Isadora Lopes Alves
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
Juan Domingo Gispert
Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
Mark E. Schmidt
Janssen Pharmaceutica NV, Beerse, Belgium
Paul Marsden
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Alexander Hammers
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Sebastien Ourselin
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
Frederik Barkhof
Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated.For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.
