A method of estimation of magnetic resonance spectroscopy using complex‐valued neural networks

Abstract

SUMMARYMRS (magnetic resonance spectroscopy) consists of collecting the nuclear magnetic resonance (NMR) spectrum of the metabolites in the living body and estimating it. In the field of MRS, we usually get an NMR spectrum by applying the Fourier transform to the real part of the NMR signal [free induction decay (FID) signal], which is a complex-valued signal. We must then measure the area of the spectral peak to estimate metabolites in the living body from the spectrum. A curve fitting technique is used for that measurement. However, this technique is not suitable for large quantities of data processing, because human intervention is necessary and is laborious. For this reason, an automatic method of estimating an NMR spectrum is required in the field of MRS. In this paper, we propose a method of estimation of an NMR spectrum by using a Hopfield complex-valued neural network and try to estimate the spectrum automatically. In addition, we consider damping state of the NMR signal and devise a method called sequential extension of section (SES). It is shown that the estimation precision of the spectrum improves when SES is used. Furthermore, SES is found to reduce the local minimum problem in Hopfield neural networks.

NMR signal processing, prediction, and structure verification with machine learning techniques

“…A totally different application of neural networks (NNs) in the context of NMR signal processing was proposed by Morita and Konishi for the estimation of the spectral resonances present in an NMR FID. The proposed procedure took advantage of the fact that NMR signals live in the complex domain, and, hence, they developed a method based on complex‐valued Hopfield networks in which the weights and thresholds for conventional networks are expanded to accommodate complex numbers.…”