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Diffusion <scp>MRI</scp> noise mapping using random matrix theory

Magnetic Resonance in Medicine · 2015 · Vol. 76(5) · pp. 1582–1593

Jelle Veraart✉(iMinds)Els Fieremans(New York University)Dmitry S. Novikov(New York University)

Abstract

Simulations and experiments show that typical diffusion MRI data exhibit sufficient redundancy that enables accurate, precise, and robust estimation of the local noise level by interpreting the principal component analysis eigenspectrum in terms of the Marchenko-Pastur distribution. Magn Reson Med 76:1582-1593, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Advanced Neuroimaging Techniques and Applications MRI in cancer diagnosis Advanced MRI Techniques and ApplicationsNoise (video)Principal component analysisRedundancy (engineering)Value noiseGradient noiseSingular value decompositionGaussian noiseEigenvalues and eigenvectorsA priori and a posterioriComputer science

MeSH terms

AlgorithmsBrainComputer SimulationData Interpretation, StatisticalHumansImage Interpretation, Computer-AssistedSensitivity and SpecificityReproducibility of ResultsModels, StatisticalPrincipal Component AnalysisDiffusion Magnetic Resonance ImagingSignal-To-Noise Ratio

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Denoising of diffusion MRI using random matrix theory