Denoising MR spectroscopic imaging data with low-rank approximations

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 1, 2013, Pages 78-89

  Nguyen, Hien M ^a   Peng, Xi ^b   Do, Minh N ^c   Liang, Zhi Pei ^c  

^a STANFORD UNIVERSITY   (United States)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^c University of Illinois at Urbana Champaign   (United States)

Author keywords

Denoising; linear prediction; low rank approximation; MR spectroscopic imaging (MRSI); MR spectroscopy; partially separable functions

Indexed keywords

DE-NOISING; LINEAR PREDICTION; LOW RANK APPROXIMATIONS; MR SPECTROSCOPY; PARTIALLY SEPARABLE FUNCTIONS; SPECTROSCOPIC IMAGING;

MAGNETIC RESONANCE SPECTROSCOPY;

SINGULAR VALUE DECOMPOSITION;

ANIMAL EXPERIMENT; ARTICLE; MOUSE; NEUROIMAGING; NOISE REDUCTION; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; SIGNAL NOISE RATIO;

ALGORITHMS; ANIMALS; BRAIN; COMPUTER SIMULATION; DATABASES, FACTUAL; HUMANS; MAGNETIC RESONANCE IMAGING; MICE; MONTE CARLO METHOD; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO;

EID: 84871739226     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2223466     Document Type: Article

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