Empirical Bayesian estimation in graphical analysis: a voxel-based approach for the determination of the volume of distribution in PET studies

Zanderigo, Francesca; Ogden, R. Todd; Bertoldo, Alessandra; Cobelli, Claudio; Mann, J. John; Parsey, Ramin V.

doi:10.1016/j.nucmedbio.2010.02.004

« Previous Next »Nuclear Medicine and Biology
Volume 37, Issue 4 , Pages 443-451, May 2010

Empirical Bayesian estimation in graphical analysis: a voxel-based approach for the determination of the volume of distribution in PET studies☆☆☆

Francesca Zanderigo
- Department of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Corresponding author. Tel.: +1 212 543 2951; fax: +1 212 543 6017.
R. Todd Ogden
- Department of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padova, Italy
Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
J. John Mann
- Department of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA
Ramin V. Parsey
- Department of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA

Received 20 April 2009; received in revised form 9 November 2009; accepted 9 February 2010. published online 08 April 2010.

Abstract

Introduction

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 [¹¹C]-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.

Keywords: Logan, Likelihood, Bayes, Clustering, Voxel, Serotonin