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 - Funding Information:
This work has been supported by internal funding from Texas Tech University. The authors are extremely grateful to Alex Lin of the department of Radiology at Harvard Medical School for creating the MRS phantom for the Texas Tech Neuroimaging Institute. We gratefully acknowledge many discussions, evaluations, and feedback regarding this research effort with Adineh Rezaei Bazkiaei and Reza Amani.
Funding Information:
Funding: This work has been supported by internal funding from Texas Tech University.
Publisher Copyright:
© 2020 by the authors.
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
SN - 1999-4893
VL - 13
JO - Algorithms
JF - Algorithms
IS - 5
M1 - 120
ER -