A New Time-Domain Frequency-Selective Quantification Algorithm

MRS signal quantitation: A review of time- and frequency-domain methods

“…Quantitation is carried out in the same domain as the domain where the signals are measured, giving more flexibility to the model function and allowing specific time-domain preprocessing. Time-domain fitting methods are usually divided into two main classes: black-box or non-interactive methods (see, e.g., [21,39,20,10,15,12]) and methods based on iterative model function fitting or interactive methods (see, e.g., [2,37,38,31,1]), referring to the degree of interaction required by the method from the user.…”

“…In the presence of water components, the frequency-selective versions of VARPRO [45] and AMARES (AMARES W [46]) are preferred and are expected to give good results for relatively well-separated peaks. However, these methods break down if nuisance peaks (i.e., peaks that are in the same frequency region but are unwanted) have large amplitudes or are close, in frequency, to the peaks of interest [21,46]. Although methods such as AMARES have been applied quite successfully to short-echo time MR spectra [47], the nuisance peaks and the more intensive user interaction tend to encourage methods based on the use of metabolite profiles since more prior knowledge is implicitly included in the model, especially information related to experimental conditions of acquisition.…”

NMR Data Analysis: A Time-Domain Parametric Approach Using Adaptive Subband Decomposition

“…Several subband methods have been proposed in the NMR literature (see e.g. [36,[40][41][42][43][44][45]). Recently, Sandgren et al [46] proposed a survey of the main subband methods and discussed their estimation performances.…”

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) …”