TY - JOUR
T1 - A novel data-driven magnetic resonance spectroscopy signal analysis framework to quantify metabolite concentration
AU - Bazgir, Omid
AU - Walden, Eric
AU - Nutter, Brian
AU - Mitra, Sunanda
N1 - Publisher Copyright:
© 2020 by the authors.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - K-means clustering
KW - Magnetic resonance spectroscopy
KW - Metabolite concentration
KW - Neurodegenerative disorders
KW - Singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=85086001818&partnerID=8YFLogxK
U2 - 10.3390/A13050120
DO - 10.3390/A13050120
M3 - Article
AN - SCOPUS:85086001818
VL - 13
JO - Algorithms
JF - Algorithms
SN - 1999-4893
IS - 5
M1 - 120
ER -