ArticleMagnetic resonance in medicine2016
Diffusion MRI noise mapping using random matrix theory.
Article in Magnetic resonance in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 504 papers.
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504 citing papers in PubMed.
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- Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity.Nature human behaviour · 2026Article
- In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging.eLife · 2026Article
- Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation.Medical physics · 2026Article
- Differentiation between glioblastomas and solid brain metastases using amide proton transfer-weighted (APTw) MRI with and without fluid suppression.Magma (New York, N.Y.) · 2026Article
- Brain network-based stratification of mental health disorders: design and cohort description of the STRATIFY and ESTRA studies.Molecular psychiatry · 2026Review
- Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles.Scientific reports · 2026Article
- Congenital blindness reduces myelination in human visual cortex.Science advances · 2026Article
- Reduced cortical gyrification in type 1 diabetes is associated with white matter integrity loss and cognitive decline.Metabolic brain disease · 2026Article
- Streamline-Based Analysis: A novel framework for tractogram-driven streamline-wise statistical inference.Research square · 2026Article
- Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation.bioRxiv : the preprint server for biology · 2026Article
444 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
purposeTo estimate the spatially varying noise map using a redundant series of magnitude MR images.
methodsWe exploit redundancy in non-Gaussian distributed multidirectional diffusion MRI data by identifying its noise-only principal components, based on the theory of noisy covariance matrices. The bulk of principal component analysis eigenvalues, arising due to noise, is described by the universal Marchenko-Pastur distribution, parameterized by the noise level. This allows us to estimate noise level in a local neighborhood based on the singular value decomposition of a matrix combining neighborhood voxels and diffusion directions.
resultsWe present a model-independent local noise mapping method capable of estimating the noise level down to about 1% error. In contrast to current state-of-the-art techniques, the resultant noise maps do not show artifactual anatomical features that often reflect physiological noise, the presence of sharp edges, or a lack of adequate a priori knowledge of the expected form of MR signal.
conclusionsSimulations 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.
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