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

doi:10.1016/j.neuroimage.2021.117821

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Neuroimage. 2021 May 15:232:117821. doi: 10.1016/j.neuroimage.2021.117821. Epub 2021 Feb 12.

Authors

Pawel J Markiewicz¹, Julian C Matthews², John Ashburner³, David M Cash⁴, David L Thomas⁵, Enrico De Vita⁶, Anna Barnes⁷, M Jorge Cardoso⁶, Marc Modat⁶, Richard Brown⁷, Kris Thielemans⁷, Casper da Costa-Luis⁸, Isadora Lopes Alves⁹, Juan Domingo Gispert¹⁰, Mark E Schmidt¹¹, Paul Marsden⁶, Alexander Hammers⁶, Sebastien Ourselin⁶, Frederik Barkhof¹²

Affiliations

¹ 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. Electronic address: http://www.nmi.cs.ucl.ac.uk.
² Division of Neuroscience & Experimental Psychology, University of Manchester, UK.
³ Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK.
⁴ Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK.
⁵ 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.
⁶ School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
⁷ Institute of Nuclear Medicine, University College London, London, UK.
⁸ 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.
⁹ Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands.
¹⁰ 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.
¹¹ Janssen Pharmaceutica NV, Beerse, Belgium.
¹² 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.

Abstract

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.

Keywords: Alzheimer’ disease; Amyloid; MR; PET; Partial volume correction; Precision; Registration.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Brain / diagnostic imaging*
Brain / metabolism*
Cohort Studies
Female
Humans
Image Processing, Computer-Assisted / methods
Image Processing, Computer-Assisted / standards*
Magnetic Resonance Imaging / methods
Magnetic Resonance Imaging / standards*
Positron-Emission Tomography / methods
Positron-Emission Tomography / standards*
Uncertainty*

Abstract

Publication types

MeSH terms

Grants and funding