An automatic analysis framework for FDOPA PET neuroimaging

and the FDOPA PET imaging working group:

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Abstract

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King’s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

Original languageEnglish
Pages (from-to)1285-1300
Number of pages16
JournalJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Volume43
Issue number8
Early online date7 Apr 2023
DOIs
Publication statusPublished - Aug 2023

Keywords

  • big data repository
  • data analysis pipeline
  • dopamine synthesis
  • FDOPA PET
  • neuroimaging biomarker

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