Direct Estimation of Neurotransmitter Activation Parameters in Dynamic PET Using Regression Neural Networks

Abstract

Current pharmacokinetic models, such as the linear parametric neurotransmitter PET (lp-ntPET) model have been developed to detect and quantify transient changes in receptor occupancy caused by variations in the concentration of endogenous neurotransmitters. However, it often performs poorly when applied at the voxel level due to high statistical noise. In this paper, we propose a new method to detect transient changes in neurotransmitter concentration in dynamic PET data using deep learning. Activation onset time and response magnitude of neurotransmitter were directly estimated using a convolution neural network (CNN) and compared to the lpntPET model. Computer simulations, as well as realistic GATE simulations were used to generate dynamic PET data, representing a [11C]raclopride study, with a known range of activation onset times and response magnitudes, across a wide range of noise levels. Results showed that the proposed neural network had better quantitative performance in estimating activation onset time and response magnitude than the conventional lp-ntPET method, especially where noise is high.