title: Empirical Bayesian estimation in graphical analysis: a voxel-based approach for the determination of the volume of distribution in PET studies.
abstract: BACKGROUND
  Total volume of distribution (V(T)) determined by graphical analysis (GA) of PET data suffers from a noise-dependent bias. Likelihood estimation in GA (LEGA) eliminates this bias at the region of interest (ROI) level, but at voxel noise levels, the variance of estimators is high, yielding noisy images. We hypothesized that incorporating LEGA V(T) estimation in a Bayesian framework would shrink estimators towards prior means, reducing variability and producing meaningful and useful voxel images.
  
  
  METHODS
  Empirical Bayesian estimation in GA (EBEGA) determines prior distributions using a two-step k-means clustering of voxel activity. Results obtained on eight [(11)C]-DASB studies are compared with estimators computed by ROI-based LEGA.
  
  
  RESULTS
  EBEGA reproduces the results obtained by ROI LEGA while providing low-variability V(T) images. Correlation coefficients between average EBEGA V(T) and corresponding ROI LEGA V(T) range from 0.963 to 0.994.
  
  
  CONCLUSIONS
  EBEGA is a fully automatic and general approach that can be applied to voxel-level V(T) image creation and to any modeling strategy to reduce voxel-level estimation variability without prefiltering of the PET data.
authors.affiliation: Department of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA. francesca.zanderigo@gmail.com
authors.forename: Francesca
authors.initials: F
authors.lastname: Zanderigo
authors.forename: R Todd
authors.initials: RT
authors.lastname: Ogden
authors.forename: Alessandra
authors.initials: A
authors.lastname: Bertoldo
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
authors.forename: J John
authors.initials: JJ
authors.lastname: Mann
authors.forename: Ramin V
authors.initials: RV
authors.lastname: Parsey
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D016011:Normal Distribution; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data*
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