Amyloid burden quantification depends on PET and MR image processing methodology

Guilherme D. Kolinger*, David V. llez García, Antoon T. M. Willemsen, Fransje E. Reesink, Bauke M. de Jong, Rudi A. J. O. Dierckx, Peter P. de Deyn, Ronald Boellaard

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer's Disease in-vivo. However, quantification can be affected by the image processing methodology applied. This study's goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their Native Space and Standard Space; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature. Intensity-related metrics were not substantially affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in Native Space avoiding non-rigid spatial transformations. For analyses in Standard Space, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in Native Space, while intensity-based metrics are more robust against differences in image processing pipelines.
Original languageEnglish
Article numbere0248122
JournalPLoS ONE
Volume16
Issue number3 March 2021
DOIs
Publication statusPublished - 1 Mar 2021

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