Spectral Quantification for High-Resolution MR Spectroscopic Imaging with Spatiospectral Constraints

Qiang Ning, Chao Ma, Fan Lam, Zhi Pei Liang

Research output: Contribution to journalArticle


Objective: To obtain reliable spectral estimation from magnetic resonance spectroscopic imaging (MRSI) data. Methods: The proposed method takes advantage of prior knowledge: 1) along the spectral dimension in the form of spectral bases, and 2) along the spatial dimensions in the form of spatial regularizations (e.g., smoothness or transform sparsity) and jointly estimates parameters from all the voxels. Results: Simulation and in vivo studies have been performed to demonstrate the performance of the proposed method. A Cramér-Rao-bound-based analysis is also provided. Conclusion: Incorporation of both spatial and spectral constraints can significantly improve spectral quantification of MRSI data. Significance: The proposed method is expected to be useful for various quantitative MRSI studies.

Original languageEnglish (US)
Article number7523255
Pages (from-to)1178-1186
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number5
StatePublished - May 2017


  • Cramér-Rao bound (CRB)
  • magnetic resonance spectroscopic imaging (MRSI)
  • sparsity constraint
  • spectral quantification

ASJC Scopus subject areas

  • Biomedical Engineering

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