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Long lasting efforts have been made to reduce radiation dose and thus thepotential radiation risk to the patient for computed tomography acquisitionswithout severe deterioration of image quality. To this end, numerousreconstruction and noise reduction algorithms have been developed, many ofwhich are based on iterative reconstruction techniques, incorporating priorknowledge in the projection or image domain. Recently, deep learning-basedmethods became increasingly popular and a multitude of papers claim everimproving performance both quantitatively and qualitatively. In this work, wefind that the lack of a common benchmark setup and flaws in the experimentalsetup of many publications hinder verifiability of those claims. We propose abenchmark setup to overcome those flaws and improve reproducibility andverifiability of experimental results in the field. In a comprehensive and fairevaluation of several deep learning-based low dose CT denoising algorithms, wefind that most methods perform statistically similar and improvements over thepast six years have been marginal at best.

Benchmarking Deep Learning-Based Low Dose CT Image Denoising Algorithms