Direct 4D parametric image reconstruction with plasma input and reference tissue models in reversible binding imaging

The most active area in brain PET ligand development and imaging continues to involve receptor/transporter studies involving reversible binding. The focus of this work has been to develop direct 4D parametric image reconstruction techniques for reversible binding imaging. Based on a recent graphical analysis formulation [1], we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images using a plasma input model. Furthermore, while previous work in the area of 4D imaging has been primarily limited to plasma input models, we sought to also develop reference tissue model schemes whereby distribution volume ratio (DVR) parametric images were reconstructed by the reference tissue model within the 4D image reconstruction task (using the cerebellum as reference). The means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for simulation (22 realizations) using a mathematical brain phantom. Images were reconstructed using standard FBP or EM methods followed by modeling, as well as the proposed direct methods. Noise vs. bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in substantial visual and quantitative accuracy improvements (over 100% noise reduction, with matched bias, in both plasma and reference-tissue input models). Notable improvements were also observed in the coefficient of variation (COV) of the estimated binding potential (BP) values, including even for the relatively low BP regions of grey and thalamus, suggesting the ability for robust parameter estimation even in such regions.