A subband Steiglitz‐McBride algorithm for automatic analysis of FID data

Abstract

Fast, accurate, and automatic extraction of parameters of nuclear magnetic resonance free induction decay (FID) signal for chemical spectroscopy is a challenging problem. Recently, the Steiglitz-McBride algorithm has been shown to exhibit superior performance in terms of speed, accuracy, and automation when applied to the extraction of T relaxation parameters for myelin water imaging of brain. Applying it to FID data reveals that it falls short of the second objective, the accuracy. Especially, it struggles with the issue of missed spectral peaks when applied to chemical samples with relatively dense frequency spectra. To overcome this issue, a preprocessing stage of subband decomposition is proposed before the application of Steiglitz-McBride algorithm to the FID signal. It is demonstrated that by doing so, a considerable improvement in accuracy is achieved. But this is not gained at the cost of the first objective, the speed. An adaptive subband decomposition is employed in conjunction with the Bayesian information criteria to carry out an efficient decomposition according to spectral content of the signal under investigation. Furthermore, adaptive subband decomposition and the Bayesian information criteria also serve to make the resulting algorithm independent of user input, which also fulfills the third objective, the automation. This makes the proposed algorithm favorable for fast, accurate, and automatic extraction of FID signal parameters.

Improving the accuracy of model-based quantitative nuclear magnetic resonance

“…Furthermore, multistage median filtering with increasing window widths w m tends to improve the smoothing results, which agrees with recent works (Arias-Castro and Donoho, 2009). Intuitively, w m = 0 corresponds to no smoothing, and the problem of Eq.…”

Progress in low-field benchtop NMR spectroscopy in chemical and biochemical analysis

“…19 Commonly-used resonance peak integration methods have been shown to suffer from certain deficiencies, particularly when applied to LF bNMR data [33]. Specifically, these are limited in their capability to resolve overlapping signals, and are inherently affected by noise, leading to the development of several novel methods of corrections and FID data analysis [34]. A recently reported Bayesian model involving lineshape and baseline corrections has been shown to account for chemical shift, relaxation, lineshape imperfections and phasing, as well as baseline distortions.…”

Multi‐component relaxation modelling in human Achilles tendon: Quantifying chemical shift information in ultra‐short echo time imaging

“…Spectral peaks were quantified using recently described parametric algorithm for quantitation of proton NMR free induction decay (FID) data 22 . The reported algorithm, based on Steiglitz‐McBride algorithm (SMA), adaptive sub‐band decomposition, and the Bayesian information criteria, estimates peak amplitudes, phases, frequencies, and decay rates from time domain FID data.…”