Improving the quality of positron emission tomography (PET) images, affectedby low resolution and high level of noise, is a challenging task in nuclearmedicine and radiotherapy. This work proposes a restoration method, achievedafter tomographic reconstruction of the images and targeting clinicalsituations where raw data are often not accessible. Based on inverse problemmethods, our contribution introduces the recently developed total generalizedvariation (TGV) norm to regularize PET image deconvolution. Moreover, westabilize this procedure with additional image constraints such as positivityand photometry invariance. A criterion for updating and adjusting automaticallythe regularization parameter in case of Poisson noise is also presented.Experiments are conducted on both synthetic data and real patient images.
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