Improved low-rank filtering of magnetic resonance spectroscopic imaging data corrupted by noise and B0 field inhomogeneity

Yan Liu, Chao Ma, Bryan A. Clifford, Fan Lam, Curtis L. Johnson, Zhi Pei Liang

Research output: Contribution to journalArticle

Abstract

Goal: To improve the signal-to-noise ratio (SNR) of magnetic resonance spectroscopic imaging (MRSI) data. Methods: A low-rank filtering method recently proposed for denoising MRSI data is extended by: 1) incorporating tissue boundary constraints to enable local low-rank filtering, and 2) integrating B0 field inhomogeneity correction by rank-minimization to make the low-rank model more effective. Results: The proposed method was validated using both simulated and in vivo MRSI data. Its denoising performance is also compared with an upper bound based on the constrained Cramér-Rao lower bound for low-rank filtering. Conclusion : Low-rank filtering can effectively improve the SNR of MRSI data corrupted by both noise and B0 field inhomogeneity. Significance: The proposed low-rank filtering method will enhance the practical utility of high-resolution MRSI, where SNR has been a limiting factor.

Original languageEnglish (US)
Article number7239580
Pages (from-to)841-849
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number4
DOIs
StatePublished - Apr 1 2016

Keywords

  • B field inhomogeneity correction
  • Denoising
  • Low-rank approximation
  • MR spectroscopic imaging
  • Partial separability
  • Subspace filtering

ASJC Scopus subject areas

  • Biomedical Engineering

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