The direct calculation of parametric images from dynamic PET data using maximum-likelihood iterative reconstruction

Abstract

The aim of this work is to calculate, directly from projection data, concise images characterizing the spatial and temporal distribution of labelled compounds from dynamic PET data. Conventionally, image reconstruction and the calculation of parametric images are performed sequentially. By combining the two processes, low-noise parametric images are obtained, using a computationally feasible parametric iterative reconstruction (PIR) algorithm. PIR is performed by restricting the pixel time-activity curves to a positive linear sum of predefined time characteristics. The weights in this sum are then calculated directly from the PET projection data, using an iterative algorithm based on a maximum-likelihood iterative algorithm commonly used for tomographic reconstruction. The ability of the algorithm to extract known kinetic components from the raw data is assessed, using data from both a phantom experiment and clinical studies. The calculated parametric images indicate differential kinetic behaviour and have been used to aid in the identification of tissues which exhibit differences in the handling of labelled compounds. These parametric images should be helpful in defining regions of interest with similar functional behaviour, and with FDG Patlak analysis.