Background: Artifacts and noise in PET imaging
are caused by multiple factors, including lowered radiotracer
dose and information loss in the form of missing
pixels or missing projections. Recently, deep learning
based algorithms have achieved promising results
in the medical imaging field, including PET denoising
or CT sinogram inpainting, especially using the
Convolutional Neural Network (CNN) and Generative
Adversarial Networks (GAN) architectures. This
article aims to compare CNN and GAN approaches
for PET sinogram missing data reconstruction task.
Methods: The end-to-end framework, from PET image
to the sinogram domain and back to PET image
domain, was proposed. The Radon transform was applied
to covert PET images into sinograms. The first
model was the CNN encoder-decoder based network
with four skip connections. The effective strategy was
applied to efficiently train more corrupted PET sinograms
by loading previously trained weights. The second
approach was the GAN network, with the generator
designed similarly as the CNN encoder-decoder,
and the discriminator containing four convolutional
layers to classify generated sinograms as artificially
generated or ground truth. The proposed framework
ended by applying filter back projection algorithm to
transform sinograms back to PET image domain.
Results: The results revealed that GAN outperformed
CNN by a small margin. The average PSNR
and SSIM scores within all five corruption levels were
41.44, 0.977, and 42.34, 0.983 when predicting missing
pixels. Differences of two metrics between CNN
and GAN were higher when predicting missing projections;
40.13, 0.866 versus 46.84, 0.989. Additionally,
GAN performed noticeably better when 90% of
sinogram data were removed, resulting in a sharper
and more detailed reconstructed image, qualitatively
comparing to CNN.
Conclusion: Different network architectures, chosen,
and objective functions, might be the reasons why
GAN performed better than CNN. Even though the
study had some limitations, the promising results were
achieved, which motivates to experiment further.