title: Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Ma
authors.affiliation: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Illinois, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Lam
authors.affiliation: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, USA.
authors.forename: Curtis L
authors.initials: CL
authors.lastname: Johnson
authors.affiliation: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Illinois, USA.
authors.forename: Zhi-Pei
authors.initials: ZP
authors.lastname: Liang
mesh_terms: D000273:Adipose Tissue / Q000378:metabolism; D000465:Algorithms; D001834:Body Water / Q000378:metabolism; D001921:Brain / Q000378:metabolism*; D064368:Healthy Volunteers; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*
keywords: 1H spectroscopic imaging; chemical shift imaging; lipid removal; partial separability; sparse sampling; union of subspaces; water removal