  Deep learning based PET image reconstruction methods have achieved promising
results recently. However, most of these methods follow a supervised learning
paradigm, which rely heavily on the availability of high-quality training
labels. In particular, the long scanning time required and high radiation
exposure associated with PET scans make obtaining this labels impractical. In
this paper, we propose a dual-domain unsupervised PET image reconstruction
method based on learned decent algorithm, which reconstructs high-quality PET
images from sinograms without the need for image labels. Specifically, we
unroll the proximal gradient method with a learnable l2,1 norm for PET image
reconstruction problem. The training is unsupervised, using measurement domain
loss based on deep image prior as well as image domain loss based on rotation
equivariance property. The experimental results domonstrate the superior
performance of proposed method compared with maximum likelihood expectation
maximazation (MLEM), total-variation regularized EM (EM-TV) and deep image
prior based method (DIP).
