Tensor denoising of multidimensional MRI data

Purpose To develop a denoising strategy leveraging redundancy in high‐dimensional data. Theory and Methods The 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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Magnetic resonance in medicine Ročník 89; číslo 3; s. 1160 - 1172
Hlavní autori: Olesen, Jonas L., Ianus, Andrada, Østergaard, Leif, Shemesh, Noam, Jespersen, Sune N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Wiley Subscription Services, Inc 01.03.2023
John Wiley and Sons Inc
Predmet:
Algorithms
Blurring
Brain - diagnostic imaging
denoising
diffusion
Diffusion Magnetic Resonance Imaging - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical analysis
Multidimensional data
Noise reduction
Patches (structures)
Phantoms, Imaging
Principal Component Analysis
Principal components analysis
random matrix theory
Redundancy
Signal-To-Noise Ratio
s—Computer Processing and Modeling
Tensors
denoising
random matrix theory
diffusion
principal component analysis
ISSN:0740-3194, 1522-2594, 1522-2594
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Purpose To develop a denoising strategy leveraging redundancy in high‐dimensional data. Theory and Methods The 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. Results Relative 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. Conclusions The MPPCA denoising technique can be extended to high‐dimensional data with improved performance for smaller patch sizes.
Bibliografia:Funding information
“la Caixa” Foundation, Grant/Award Number: 100010434; Danish Ministry of Science, Innovation, and Education, Grant/Award Number: MINDLab; Danish National Research Foundation, Grant/Award Number: CFIN; European Regional Development Fund, Grant/Award Number: LISBOA‐01‐0145‐FEDER‐02217; Fundação para a Ciência e Tecnologia, H2020 European Research Council, Grant/Award Numbers: 100010434; agreement 679058; Lisboa Regional Operational Programme, Grant/Award Number: LISBOA‐01‐0145‐FEDER‐02217; Velux Fonden, Grant/Award Numbers: ARCADIA; grant 00015963
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Funding information “la Caixa” Foundation, Grant/Award Number: 100010434; Danish Ministry of Science, Innovation, and Education, Grant/Award Number: MINDLab; Danish National Research Foundation, Grant/Award Number: CFIN; European Regional Development Fund, Grant/Award Number: LISBOA‐01‐0145‐FEDER‐02217; Fundação para a Ciência e Tecnologia, H2020 European Research Council, Grant/Award Numbers: 100010434; agreement 679058; Lisboa Regional Operational Programme, Grant/Award Number: LISBOA‐01‐0145‐FEDER‐02217; Velux Fonden, Grant/Award Numbers: ARCADIA; grant 00015963
ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.29478