  Objective Positron emission tomography (PET) allows imaging of
patho-physiological information as a form of rate constants from a dynamic
image. The rate constant image(s) may be affected from noise on the dynamic
image. We introduced an artificial intelligence technique of deep image prior
(DIP) to reduce noise on dynamic images. Method We utilized a phantom filled
with 18F-F- and 11C-flumazenil solutions in the main and sub-cylinders,
respectively. The phantom was scanned by a Biograph mCT and dynamic images were
obtained. DIP was applied to all slices involved in the dynamic images while
introducing an index for choosing an optimal epoch with minimize the degree of
noise. Then, decay rate images were generated and quantitative accuracy and
quality were measured in the images. Results The obtained decay rates on images
were not significantly different from those of the reference values.
Coefficient of variances (CV) were smaller using DIP-based images than those
from original images for the reconstructed and decay rate images, both in the
11C and 18F filled regions. Conclusion The present method for choosing a
generated image during DIP is feasible for noise reduction in dynamic PET
images, and for obtaining rate constant images with less noise.
