Journal article
Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations
IEEE transactions on biomedical engineering, Vol.60(1), pp.78-89
01/01/2013
DOI: 10.1109/TBME.2012.2223466
PMCID: PMC3800688
PMID: 23070291
Abstract
This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.
Details
- Title: Subtitle
- Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations
- Creators
- Hien M. Nguyen - Stanford UniversityXi Peng - Shenzhen Institutes of Advanced TechnologyMinh N. Do - University of Illinois Urbana-ChampaignZhi-Pei Liang - University of Illinois Urbana-Champaign
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on biomedical engineering, Vol.60(1), pp.78-89
- Publisher
- IEEE
- DOI
- 10.1109/TBME.2012.2223466
- PMID
- 23070291
- PMCID
- PMC3800688
- ISSN
- 0018-9294
- eISSN
- 1558-2531
- Language
- English
- Date published
- 01/01/2013
- Academic Unit
- Radiology
- Record Identifier
- 9984446260502771
Metrics
9 Record Views