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.
Highlights
Nuclear magnetic resonance (NMR) is an important biochemical technique for the in vitro determination of protein structure and protein-drug interaction studies [1,2], which is being widely used in the field of in-cell NMR [3,4,5,6]
Magnetic resonance spectroscopy (MRS), which is based on NRM principles, is widely being used along with magnetic resonance imaging (MRI) to acquire detailed information about the tissue structures for medical diagnosis [7,8,9]
The existing metabolites’ basis set must be selected, because TARQUIN, by default, uses basis set of 26 metabolites and macromolecules to fit the MR spectra
Summary
Nuclear magnetic resonance (NMR) is an important biochemical technique for the in vitro determination of protein structure and protein-drug interaction studies [1,2], which is being widely used in the field of in-cell NMR [3,4,5,6]. Magnetic resonance (MR) spectra analysis methods can be classified as either black box (data-driven) or basis sets methods [8]. Basis set methods incorporate prior knowledge of metabolites chemical structures in both in-vivo NMR (e.g., LC ModelTM [13], TARQUIN [14]), and in-vitro NMR [15], while the black box does not incorporate prior knowledge, and are completely data-driven, such as QUEST [16]. Black box methods have been shown effective on sparse data, and long echo time (TE) acquisition; the main drawback
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