Joint application of a statistical optimization process and Empirical Mode Decomposition to Magnetic Resonance Sounding Noise Cancelation

Abstract

The signal quality of Magnetic Resonance Sounding (MRS) measurements is a crucial criterion. The accuracy of the estimation of the signal parameters (i.e. E0 and T2⁎) strongly depends on amplitude and conditions of ambient electromagnetic interferences at the site of investigation. In this paper, in order to enhance the performance in the noisy environments, a two-step noise cancelation approach based on the Empirical Mode Decomposition (EMD) and a statistical method is proposed. In the first stage, the noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of the EMD algorithm. Afterwards based on an automatic procedure the noisy IMFs are detected, and then the partly de-noised signal is reconstructed through the no-noise IMFs. In the second stage, the signal obtained from the initial section enters an optimization process to cancel the remnant noise, and consequently, estimate the signal parameters. The strategy is tested on a synthetic MRS signal contaminated with Gaussian noise, spiky events and harmonic noise, and on real data. By applying successively the proposed steps, we can remove the noise from the signal to a high extent and the performance indexes, particularly signal to noise ratio, will increase significantly.