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A Novel Data-Driven Magnetic Resonance Spectroscopy Signal Analysis Framework to Quantify Metabolite Concentration

1
Department of Electrical and Computer Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
2
Texas Tech Neuroimaging Institute, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(5), 120; https://doi.org/10.3390/a13050120
Received: 20 April 2020 / Accepted: 7 May 2020 / Published: 10 May 2020
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. View Full-Text
Keywords: magnetic resonance spectroscopy; singular value decomposition; K-means clustering; metabolite concentration; neurodegenerative disorders magnetic resonance spectroscopy; singular value decomposition; K-means clustering; metabolite concentration; neurodegenerative disorders
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Bazgir, O.; Walden, E.; Nutter, B.; Mitra, S. A Novel Data-Driven Magnetic Resonance Spectroscopy Signal Analysis Framework to Quantify Metabolite Concentration. Algorithms 2020, 13, 120.

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