Benchmarking Deep Learning-Based Low-Dose CT Image Denoising Algorithms

Long lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography acquisitions without severe deterioration of image quality. To this end, numerous reconstruction and noise reduction algorithms have been developed, many of which are based on iterative reconstruction techniques, incorporating prior knowledge in the projection or image domain. Recently, deep learning-based methods became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. In this work, we find that the lack of a common benchmark setup and flaws in the experimental setup of many publications hinder verifiability of those claims. We propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. In a comprehensive and fair evaluation of several deep learning-based low dose CT denoising algorithms, we find that most methods perform statistically similar and improvements over the past six years have been marginal at best.