Locally weighted total variation denoising for PSF modeling artifact suppression in PET reconstruction

Incorporating the point spread function (PSF) into the iterative MLEM reconstruction of PET images introduces contrast and size dependent ringing and over enhancement artifacts. We previously developed a new method, called TV-PSF-MLEM, to suppress these artifacts based on the introduction of a locally-weighted total variation regularization within the MLEM reconstruction algorithm. On non-noisy PET measures, we proposed to compute the TV spatial weights based on the point-wise convergence rate of a preliminary MLEM reconstruction, for each voxel. In this work we extend the TV-PSF-MLEM weighting scheme to noisy measures introducing a noise-independent weighting scheme. We compare its performance to a state of the art PET denoising method. Results on numerical phantoms show that the TV-PSF-MLEM offers substantial advantages in the recovery of small cylinders and gains in contrast recovery of larger cylinders.