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Removal of nuisance signals from limited and sparse 1 H MRSI data using a union‐of‐subspaces model
Author(s) -
Ma Chao,
Lam Fan,
Johnson Curtis L.,
Liang ZhiPei
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25635
Subject(s) - magnetic resonance spectroscopic imaging , subspace topology , nuisance parameter , linear subspace , voxel , computer science , pattern recognition (psychology) , artificial intelligence , mathematics , magnetic resonance imaging , statistics , radiology , medicine , geometry , estimator
Purpose To remove nuisance signals (e.g., water and lipid signals) for 1 H MRSI data collected from the brain with limited and/or sparse (k, t)‐space coverage. Methods A union‐of‐subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)‐space coverage. Results The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k‐space coverage, the proposed method produced “lipid‐free” spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)‐space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Conclusion Nuisance signals in 1 H MRSI data reside in low‐dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from 1 H MRSI data even when they have limited and/or sparse coverage of (k, t)‐space. The proposed method should prove useful especially for accelerated high‐resolution 1 H MRSI of the brain. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc. Magn Reson Med 75:488–497, 2016. © 2015 Wiley Periodicals, Inc.