Voxelwise quantification of [11 C](R)-rolipram PET data: A comparison between model-based and data-driven methods

Gaia Rizzo, Mattia Veronese, Paolo Zanotti-Fregonara, Alessandra Bertoldo

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [11 C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic V T estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise V T estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R 2 =0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R 2 >0.96). H-BFM yielded the most reliable voxelwise quantification of [11 C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only V T estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness.

Original languageEnglish (US)
Pages (from-to)1032-1040
Number of pages9
JournalJournal of Cerebral Blood Flow and Metabolism
Volume33
Issue number7
DOIs
StatePublished - Jul 2013

Keywords

  • [11C](R)-rolipram
  • parametric images
  • positron-emission tomography
  • spectral analysis
  • voxelwise quantification

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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