Neural estimation of kinetic rate constants from dynamic PET-scans

摘要

A feedforward neural net is trained to invert a simple three compartment model describing the tracer kinetics involved in the metabolism of [/sup 18/F]fluorodeoxyglucose in the human brain. The network can estimate rate constants from positron emission tomography sequences and is about 50 times faster than direct fitting of rate constants using the parametrized transients of the compartment model.