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• W2152208725 abstract "Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically realized as a spatially invariant estimate of the noise distribution. This is not an ideal practice in diffusion tensor imaging because the noise distribution is usually spatially varying due to the use of fast imaging and noise suppression techniques. A new estimation approach for spatially varying noise fields (NFs) is proposed in this article. The approach is based on a noise invariance property in scenarios in which more than one image, each with potentially different signal levels, is acquired on each slice, as in diffusion-weighted MRI. This technique leads to improved NF estimates in simulations, phantom experiments and in vivo studies when compared to traditional NF estimators that use regional variability or background intensity histograms. The proposed method reduces the NF estimation error by a factor of 100 in simulations, shows a strong linear correlation (R2=0.99) between theoretical and estimated noise changes in phantoms and demonstrates consistent (<5% variability) NF estimates in vivo. The advantages of spatially varying NF estimation are demonstrated for power analysis, outlier detection and tensor estimation." @default.
• W2152208725 created "2016-06-24" @default.
• W2152208725 creator A5048433989 @default.
• W2152208725 creator A5066682675 @default.
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• W2152208725 date "2009-07-01" @default.
• W2152208725 modified "2023-10-16" @default.
• W2152208725 title "Estimation and application of spatially variable noise fields in diffusion tensor imaging" @default.
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• W2152208725 doi "https://doi.org/10.1016/j.mri.2009.01.001" @default.
• W2152208725 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2733233" @default.
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• W2152208725 hasPublicationYear "2009" @default.
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