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Abstract #0165

EDDEN: Towards a framework for Evaluating Diffusion MRI DENoising approaches

Jose Pedro Manzano Patron1,2, Steen Moeller3, Essa Yacoub3, and Stamatios Sotiropoulos1
1Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 2Precision Imaging Beacon, University of Nottingham, Nottingham, United Kingdom, 3Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States


Denoising diffusion MRI data has gained significant interest over the last years, due to the inherently low-SNR in the dMRI signal. Despite the existence of a number of denoising algorithms, open questions exist on how, and even whether, to denoise data. A consistent set of evaluations that comprehensively characterise newly-developed approaches and their impact on downstream applications is lacking for providing insight to these questions. Here we propose EDDEN, a framework for Evaluating DMRI DENoising approaches, consisting of a set of unique data and assessments. We demonstrate its use using 3 exemplar denoising methods (NLM, MPPCA and NORDIC).

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