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Objective Positron emission tomography (PET) allows imaging ofpatho-physiological information as a form of rate constants from a dynamicimage. The rate constant image(s) may be affected from noise on the dynamicimage. We introduced an artificial intelligence technique of deep image prior(DIP) to reduce noise on dynamic images. Method We utilized a phantom filledwith 18F-F- and 11C-flumazenil solutions in the main and sub-cylinders,respectively. The phantom was scanned by a Biograph mCT and dynamic images wereobtained. DIP was applied to all slices involved in the dynamic images whileintroducing an index for choosing an optimal epoch with minimize the degree ofnoise. Then, decay rate images were generated and quantitative accuracy andquality were measured in the images. Results The obtained decay rates on imageswere not significantly different from those of the reference values.Coefficient of variances (CV) were smaller using DIP-based images than thosefrom original images for the reconstructed and decay rate images, both in the11C and 18F filled regions. Conclusion The present method for choosing agenerated image during DIP is feasible for noise reduction in dynamic PETimages, and for obtaining rate constant images with less noise.

Applicability test for reducing noise on PET dynamic images using phantom applying deep image prior