A novel data-driven magnetic resonance spectroscopy signal analysis framework to quantify metabolite concentration

Omid Bazgir, Eric Walden, Brian Nutter, Sunanda Mitra

Research output: Contribution to journalArticlepeer-review

Abstract

Developing tools for precise quantification of brain metabolites using magnetic resonance spectroscopy (MRS) is an active area of research with broad application in non-invasive neurodegenerative disease studies. The tools are mainly developed based on black box (data-driven), or basis sets approaches. In this study, we offer a multi-stage framework that integrates data-driven and basis sets methods. We first use truncated Hankel singular value decomposition (HSVD) to decompose free induction decay (FID) signals into single tone FIDs, as the data-driven stage. Subsequently, single tone FIDs are clustered into basis sets while using initialized K-means with prior knowledge of the metabolites, as the basis set stage. The generated basis sets are fitted with the magnetic resonance (MR) spectra while using a linear constrained least square, and then the metabolite concentration is calculated. Prior to using our proposed multi-stage approach, a sequence of preprocessing blocks: water peak removal, phase correction, and baseline correction (developed in house) are used.

Original languageEnglish
Article number120
JournalAlgorithms
Volume13
Issue number5
DOIs
StatePublished - May 1 2020

Keywords

  • K-means clustering
  • Magnetic resonance spectroscopy
  • Metabolite concentration
  • Neurodegenerative disorders
  • Singular value decomposition

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