Direct 4D PET MLEM reconstruction of parametric images using the simplified reference tissue model with the basis function method

The purpose of this work is to assess the one-step late maximum likelihood expectation maximization (OSL-MLEM) direct 4D PET reconstruction algorithm when using the simplified reference tissue model with the basis function method (SRTM-BFM). To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component. We therefore extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 2D and 3D + time data sets (with use of [11C]raclopride time-activity curves (TACs) from real data) and on real data sets acquired with the high resolution research tomograph (HRRT). Furthermore, this work investigates the impact of correcting the TACs by the frame length, as assumed by most conventional kinetic parameter estimation techniques (applied post-reconstruction) used in practice. The performance of the proposed method is evaluated by comparing binding potential (BP) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation. It is shown that, for the 2D + time simulation, SRTM-BFM within the OSL-MLEM framework delivers lower %BIAS and %CV, and thus lower %RMSE, in BP estimates compared to the post reconstruction approach, while for the real 3D data set the method delivers lower spatial %CV, in addition to better BP parametric image quality, when using resolution modeling. Finally, frame length correction can be applied but correct weighting is necessary to obtain the best performance.