Research Repository

Semiblind Spectral Factorization Approach for Magnetic Resonance Spectroscopy Quantification

Ferdowsi, Saideh and Abolghasemi, Vahid (2018) 'Semiblind Spectral Factorization Approach for Magnetic Resonance Spectroscopy Quantification.' IEEE Transactions on Biomedical Engineering, 65 (8). pp. 1717-1724. ISSN 0018-9294

Full text not available from this repository. (Request a copy)


An observed magnetic resonance (MR) spectrum is composed of a set of metabolites spectrum, baseline, and noise. Quantification of metabolites of interest in the MR spectrum provides great opportunity for early diagnosis of dangerous disease such as brain tumors. In this paper, a novel spectral factorization approach based on singular spectrum analysis (SSA) is proposed to quantify magnetic resonance spectroscopy (MRS). In addition, baseline removal is performed in this study. The proposed method is a semiblind spectral factorization algorithm that jointly uses observed signal and prior knowledge about metabolites of interest to improve metabolite separation. In order to incorporate prior knowledge about metabolites of interest, a new covariance matrix is suggested that exploits correlation between the observed nuclear magnetic resonance signal and prior knowledge. The objectives of the proposed method are 1) removing baseline in frequency domain using SSA; 2) extracting the underlying components of MRS signal based on the suggested novel covariance matrix; and 3) reconstructing metabolite of interest by combining some of the extracted components using a novel cost function. Performance of the proposed method is evaluated using both synthetic and real MRS signals. The obtained results show the effectiveness of the proposed technique to accurately remove baseline and extract metabolites of MRS signal.

Item Type: Article
Uncontrolled Keywords: Magnetic resonance spectroscopy (MRS); singular spectrum analysis (SSA); baseline removal; metabolite quantification; brain tumors
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Apr 2020 13:52
Last Modified: 23 Sep 2022 19:33

Actions (login required)

View Item View Item