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This study aimed to propose a denoising method for dynamic contrast-enhancedcomputed tomography (DCE-CT) perfusion studies using a three-dimensional deepimage prior (DIP), and to investigate its usefulness in comparison with totalvariation (TV)-based methods with different regularization parameter (alpha)values through simulation studies. In the proposed DIP method, the DIP wasincorporated into the constrained optimization problem for image denoising as asimultaneous spatial and temporal regularizer, which was solved using thealternating direction method of multipliers. In the simulation studies, DCE-CTimages were generated using a digital brain phantom and their noise level wasvaried using the X-ray exposure noise model with different exposures (15, 30,50, 75, and 100 mAs). Cerebral blood flow (CBF) images were generated from theoriginal contrast enhancement (CE) images and those obtained by the DIP and TVmethods using block-circulant singular value decomposition. The quality of theCE images was evaluated using the peak signal-to-noise ratio (PSNR) andstructural similarity index (SSIM). To compare the CBF images obtained by thedifferent methods and those generated from the ground truth images, linearregression analysis was performed. When using the DIP method, the PSNR and SSIMwere not significantly dependent on the exposure, and the SSIM was the highestfor all exposures. When using the TV methods, they were significantly dependenton the exposure and alpha values. The results of the linear regression analysissuggested that the linearity of the CBF images obtained by the DIP method wassuperior to those obtained from the original CE images and by the TV methods.Our preliminary results suggest that the DIP method is useful for denoisingDCE-CT images at ultra-low to low exposures and for improving the accuracy ofthe CBF images generated from them.

Denoising method for dynamic contrast-enhanced CT perfusion studies  using three-dimensional deep image prior as a simultaneous spatial and  temporal regularizer