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      Evaluation of MRI Denoising Methods Using Unsupervised Learning

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          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.

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          Most cited references33

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Image Quality Assessment: From Error Visibility to Structural Similarity

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              Adam: A method for stochastic optimization.

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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                04 June 2021
                2021
                : 4
                : 642731
                Affiliations
                [ 1 ]Department of Computer Science, University of Colorado Colorado Springs, Colorado Springs, CO, United States
                [ 2 ]Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA, United States
                Author notes

                Edited by: Shuai Li, Swansea University, United Kingdom

                Reviewed by: Shivanand Sharanappa Gornale, Rani Channamma University, India

                Ameer Tamoor Khan, Hong Kong Polytechnic University, China

                *Correspondence: Marc Moreno López, mmorenol@ 123456uccs.edu

                This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence

                Article
                642731
                10.3389/frai.2021.642731
                8212039
                34151253
                02bc169f-6f88-4524-ab62-fcea8ff71cb3
                Copyright © 2021 Moreno López, Frederick and Ventura.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 December 2020
                : 17 May 2021
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: 1R15GM128166-01
                Categories
                Artificial Intelligence
                Original Research

                deep learning,denoising,k-space,mri,unsupervised
                deep learning, denoising, k-space, mri, unsupervised

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