Dynamic Dual-Tracer PET Reconstruction

Abstract

Although of important medical implications, simultaneous dual–tracer positron emission tomography reconstruction remains a challenging problem, primarily because the photon measurements from dual tracers are overlapped. In this paper, we propose a simultaneous dynamic dual–tracer reconstruction of tissue activity maps based on guidance from tracer kinetics. The dual–tracer reconstruction problem is formulated in a state–space representation, where parallel compartment models serve as continuous–time system equation describing the tracer kinetic processes of dual tracers, and the imaging data is expressed as discrete sampling of the system states in measurement equation. The image reconstruction problem has therefore become a state estimation problem in a continuous–discrete hybrid paradigm, and H _∞ filtering is adopted as the estimation strategy. As H _∞ filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for the dual-tracer PET imaging system where the statistical properties of measurement data and system uncertainty are not available a priori, even when there are disturbances in the kinetic parameters. Experimental results on digital phantoms, Monte Carlo simulations and physical phantoms have demonstrated the superior performance.