  Convolutional neural networks (CNNs) have recently achieved remarkable
performance in positron emission tomography (PET) image reconstruction. In
particular, CNN-based direct PET image reconstruction, which directly generates
the reconstructed image from the sinogram, has potential applicability to PET
image enhancements because it does not require image reconstruction algorithms,
which often produce some artifacts. However, these deep learning-based, direct
PET image reconstruction algorithms have the disadvantage that they require a
large number of high-quality training datasets. In this study, we propose an
unsupervised direct PET image reconstruction method that incorporates a deep
image prior framework. Our proposed method incorporates a forward projection
model with a loss function to achieve unsupervised direct PET image
reconstruction from sinograms. To compare our proposed direct reconstruction
method with the filtered back projection (FBP) and maximum likelihood
expectation maximization (ML-EM) algorithms, we evaluated using Monte Carlo
simulation data of brain [$^{18}$F]FDG PET scans. The results demonstrate that
our proposed direct reconstruction quantitatively and qualitatively outperforms
the FBP and ML-EM algorithms with respect to peak signal-to-noise ratio and
structural similarity index.
