This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 644
Type: Invited
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #305983
Title: Mixture Modeling Strategies for Dynamic PET Data
Author(s): Todd Ogden*+ and Huiping Jiang
Companies: Columbia University and New York State Psychiatric Institute
Address: Dept. of Biostatistics, New York, NY, 10032,
Keywords: mixture model ; PET ; brain imaging ; kinetic modeling

Standard kinetic modeling of dynamic PET data requires specifying a compartment structure and fitting the appropriate kinetic model using nonlinear least squares algorithms separately for each voxel in the brain. This approach is not completely satisfactory because of a natural reluctance researchers have to specifying a particular compartmental model to be applied to all voxels, and in addition, there are parameter identifiability issues for all but the simplest models. Methodology for modeling dynamic PET data will be presented that works by "borrowing strength" across all voxels, expressing each voxel's data as a linear combination of a small number of components that are estimated from the data. Though based on a kinetic modeling structure, it does not require a choice of compartmental system and allows for data-adaptive choice of model order.

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