Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.