ArticleMagnetic resonance in medicine2023
Tensor denoising of multidimensional MRI data.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Axon Diameter Mapping From Myelin Water Diffusion MRI.IEEE transactions on medical imaging · 2026Article
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- Tensor-Valued Diffusion MRI for Microstructural Assessment During Stereotactic Radiotherapy of Brain Metastases: A Feasibility Study.Tomography (Ann Arbor, Mich.) · 2026Article
- Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lung at 0.55T.Magnetic resonance in medicine · 2026Article
- Underpinning hierarchical anisotropic structure formation in soy protein high-moisture extrudates by Magnetic Resonance Imaging.Current research in food science · 2026Article
- MRI investigation of orientation-dependent changes in microstructure and function in a mouse model of mild traumatic brain injury.Acta neuropathologica communications · 2025Article
- Microstructural maturation of the adult mouse brain.Communications biology · 2025Article
- Comparison of Low-Rank Denoising Methods for Dynamic Deuterium MRSI at 7 T.NMR in biomedicine · 2025Article
- Feasibility of High-Resolution Deuterium Metabolic Imaging of the Human Kidney Using Concentric Ring Trajectory Sampling at 7T.NMR in biomedicine · 2025Article
- Functional imaging of hippocampal layers using VASO and BOLD on the Next Generation (NexGen) 7T Scanner.bioRxiv : the preprint server for biology · 2025Article
- Robust-tedana: An automated denoising pipeline for multi-echo fMRI data.bioRxiv : the preprint server for biology · 2025Article
- Romer-EPTI: Rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale diffusion MRI and microstructure imaging.Magnetic resonance in medicine · 2025Article
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5 authors.
Funding
Abstract
purposeTo develop a denoising strategy leveraging redundancy in high-dimensional data. THEORY AND
methodsThe SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components.
resultsRelative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases.
conclusionsThe MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.
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