AUTHOR=Moreno López Marc , Frederick Joshua M. , Ventura Jonathan TITLE=Evaluation of MRI Denoising Methods Using Unsupervised Learning JOURNAL=Frontiers in Artificial Intelligence VOLUME=Volume 4 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.642731 DOI=10.3389/frai.2021.642731 ISSN=2624-8212 ABSTRACT=

In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. The first method is based on Stein’s Unbiased Risk Estimator, while the second approach is based on a blindspot network, which limits the network’s receptive field. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI. These datasets contain information about the complex image space which will be used for denoising purposes. Both networks are compared against a state-of-the-art algorithm, Non-Local Means (NLM) using quantitative and qualitative measures. For most given metrics and qualitative measures, both networks outperformed NLM, and they prove to be reliable denoising methods.