Positron emission tomography (PET) is a molecular imaging technique which produces tomographic images of functional processes in the body. It thus has significant clinical applications in oncology, cardiology and neurology, as well as increasing importance in drug discovery. We are interested in developing more accurate and robust reconstruction strategies for dynamic PET imaging, in an attempt to study the drug-tissue interaction process.
Dynamic PET reconstruction is a challenging issue due to the spatio-temporal nature and complexity of the data. Conventional frame-by-frame approaches fail to explore the temporal information of dynamic PET data, and may lead to inaccurate results due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction. We have proposed a tracer kinetics guided reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a state-space representation, where compartment model serves as a continuous-time system equation to describe the tracer kinetic processes, and the imaging data is expressed as discrete sampling of the system states in a measurement equation. The reconstruction problem has therefore become a state estimation problem in a continuous-discrete hybrid paradigm, and sampled-data H-infinity filtering is applied to for the estimation. As H-infinity filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for dynamic PET imaging where the statistical properties of measurement data and system uncertainty are not available a priori.