Bootstrapped DEPICT for error estimation in PET functional imaging

Abstract

Basis pursuit denoising is a new approach for data-driven estimation of parametric images from dynamic positron emission tomography (PET) data. At present, this kinetic modeling technique does not allow for the estimation of the errors on the parameters. These estimates are useful when performing subsequent statistical analysis, such as, inference across a group of subjects or when applying partial volume correction algorithms. The difficulty with calculating the error estimates is a consequence of using an overcomplete dictionary of kinetic basis functions. In this paper, a bootstrap approach for the estimation of parameter errors from dynamic PET data is presented. This paper shows that the bootstrap can be used successfully to compute parameter errors on a region of interest or parametric image basis. Validation studies evaluate the methods performance on simulated and measured PET data ([ 11C]Diprenorphine—opiate receptor and [ 11C]Raclopride—dopamine D 2 receptor). The method is presented in the context of PET neuroreceptor binding studies, however, it has general applicability to a wide range of PET/SPET radiotracers in neurology, oncology and cardiology.