Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model

Chao Ma, Fan Lam, Curtis L. Johnson, Zhi Pei Liang

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose To remove nuisance signals (e.g., water and lipid signals) for 1H 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 1H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from 1H 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 1H MRSI of the brain.

Original languageEnglish (US)
Pages (from-to)488-497
Number of pages10
JournalMagnetic Resonance in Medicine
Volume75
Issue number2
DOIs
StatePublished - Feb 1 2016

Keywords

  • H spectroscopic imaging
  • chemical shift imaging
  • lipid removal
  • partial separability
  • sparse sampling
  • union of subspaces
  • water removal

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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