PET reconstruction with non-smooth gradient-based priors

Quantitative PET imaging is hindered by limited spatial resolution and high Poisson noise. A way to overcome those limitations, especially in brain PET/MR examinations, is the inclusion of anatomical prior knowledge in the image reconstruction process. Recently, the concept of Parallel Level Sets (PLS) [1]-[3] has been introduced as a promising anatomical prior for PET reconstruction. This prior relies on the exchange of gradient information. However, PLS is a non-smooth function which hampers the optimization process in iterative reconstruction. In this proceeding we show how to use the EM-TV algorithm by Sawatzki et al. [4] to solve the non-smooth PLS-regularized PET reconstruction problem efficiently which enables application to 3D clinical PET data.