TY - JOUR
T1 - Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging
AU - Markiewicz, Pawel J
AU - Matthews, Julian C
AU - Ashburner, John
AU - Cash, David M
AU - Thomas, David L
AU - Vita, Enrico De
AU - Barnes, Anna
AU - Cardoso, M Jorge
AU - Modat, Marc
AU - Brown, Richard
AU - Thielemans, Kris
AU - da Costa-Luis, Casper
AU - Alves, Isadora Lopes
AU - Lopez, Juan Domingo Gispert
AU - Schmidt, Mark
AU - Marsden, Paul
AU - Hammers, Alexander
AU - Ourselin, Sebastien
AU - Barkhof, Frederik
N1 - Funding Information:
The research was also supported by the Medical Research Council (MR/N025792/1) for the Dementias Platform UK MR-PET Partnership. This research was also supported by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme and EFPIA (This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein). The scans were acquired as part of the Insight 46 which was principally funded by grants from Alzheimer Research UK, the Medical Research Council Dementias Platform UK and the Wolfson Foundation (PR/ylr/18575). Florbetapir amyloid tracer is kindly provided by Avid Radiopharmaceuticals (a wholly owned subsidiary of Eli Lilly) who had no part in the design of the study. JA is supported by core funding from Wellcome [WT 203147/Z/16/Z]. EDV is supported by the Wellcome / EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. FB is supported by the NIHR biomedical research centre at UCLH. Special thanks goes to Prof. Jonathan Schott of Dementia Research Centre, UCL for the help with the data. Thanks to Dr. Stefan Vollmar and Michael Su for the help with VINCI software.
Publisher Copyright:
© 2021 The Author(s)
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5/15
Y1 - 2021/5/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101825115&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2021.117821
DO - 10.1016/j.neuroimage.2021.117821
M3 - Article
C2 - 33588030
SN - 1053-8119
VL - 232
SP - 117821
JO - NeuroImage
JF - NeuroImage
M1 - 117821
ER -