Quantification of Human Brain Metabolites from in Vivo1H NMR Magnitude Spectra Using Automated Artificial Neural Network Analysis

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

“…The concept of using ANN for the analysis of MRSI is not new. Recently ANN based approaches for automated quantification of MRSI data have been reported [15,16]. As pointed out earlier these methods suffer from a number of limitations.…”

“…The areas of the Lorentzian and Gaussian peaks can be expressed as [29] A(1) = bhπ [16] A(g) = bh π ln(2) [17] The final area of the peak is computed as…”

“…The ability of ANN for classification of spectra for various pathologies has been demonstrated [6][7][8][9][10][11][12][13][14]. However, relatively little attention has been paid to metabolite quantification using ANN [15][16]. Recently, ANN has been used to develop automated methods to quantify MRS data [15][16].…”

“…However, relatively little attention has been paid to metabolite quantification using ANN [15][16]. Recently, ANN has been used to develop automated methods to quantify MRS data [15][16]. These studies demonstrated a high correlation between the areas of metabolite peaks computed with ANN and LF methods.…”

Quantification Improvements of 1H MRS Signals

“…• Time-and frequency domain fitting using a linear combination of individual peaks/profiles to fit the spectra QUEST (Ratiney et al, 2004), AQSES (Poullet et al, 2007) LCModel (Provencher, 1993;2001) • Time-domain estimation of parameters using prior knowledge Young et al, 1998), AMARES • Time-domain non-iterative fitting methods such as HLSVD (Barkhuijsen et al, 1987;Chen et al, 1996;Dologlou et al, 1998;Laudadio et al, 2002;Pijnappel et al, 1992;van den Boogaart, 1997) • Iterative time-and frequency domain fitting (Slotboom et al, 1998) • Semi-parametric fitting (Elster et al, 2005) • Time-domain variable projection (VARPRO) (Cavassila et al, 1999;van der Veen et al, 1988) • Time domain fitting of one peak at a time and wavelet modeling for the baseline (Dong et al, 2006;Romano et al, 2002) • Constrained least squares (TARQUIN) (Reynolds et al, 2006;Wilson et al, 2011) • Genetic algorithms (Metzger et al, 1996) • Fast Padé Transform (Belkić&Belkić, 2006) • Artificial Neural Networks (Bhat et al, 2006;Hiltunen et al, 2002) • Sparse representation (Guo et al, 2010) • Circular fitting (Gabr et al, 2006) • Principal Component Analysis (PCA), Independent Component Analysis (ICA) (Hao et al, 2009;Stoyanova & Brown, 2001) …”