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Voxelwise Quantification of [ 11 C]( R )-Rolipram PET Data: A Comparison Between Model-Based and Data-Driven Methods

2013; SAGE Publishing; Volume: 33; Issue: 7 Linguagem: Inglês

10.1038/jcbfm.2013.43

1559-7016

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

Advanced Neuroimaging Techniques and Applications

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.