Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLS filtering

Abstract

One of the main vital signs used in patient monitoring during Magnetic Resonance Imaging (MRI) is Electro-Cardio-Gram (ECG). Unfortunately, magnetic fields gradients induce artefacts which severely affect ECG quality. Adaptive Noise Cancelling (ANC) is one of the preferred techniques for artefact removal. ANC involves the adaptive estimation of the impulse response of the system constituted by the MRI equipment, the patient and the ECG recording device. Least Mean Square (LMS) adaptive filtering has been traditionally employed because of its simplicity: anyway, it requires the choice of a step-size parameter, whose proper value for the specific application must be estimated case by case: an improper choice could yield slow convergence and unsatisfactory behaviour. Recursive Least Square (RLS) algorithm has, potentially, faster convergence while not requiring any parameter. As far as the authors' knowledge, there is no systematic analysis of performances of RLS in this scenario. In this study we evaluated the performance of RLS for adaptive removal of artefacts induced by magnetic field gradients on ECG in MRI, in terms of efficacy of suppression. Tests have been made on real signals, acquired via an expressly developed system. A comparison with LMS was made on the basis of opportune performance indices. Results indicate that RLS is superior to LMS in several respects.

Finite element analysis of magnetohydrodynamic effects on blood flow in an aneurysmal geometry

“…6 In this direction, there have been substantial efforts in removing the noise from the signal by proper filtering. [7][8][9] The nature of the problem however remains and might get more intense while moving gradually from the MRI to the time-dependent PC-MRI which can provide a picture of the hemodynamics. In this direction, tube or rotating phantoms were developed to test if PC-MRI introduces visualization errors determining the accuracy degree of the technique.…”

Adaptive noise cancellation to suppress electrocardiography artifacts during real‐time interventional MRI

“…1b, adaptive filtering operates on the three reference gradient signals obtained from the MR scanner to produce the estimated gradient‐induced artifacts from each axis, which are summed together ( A *[ n ]). For noise estimation, the reference signals should be well correlated in time with the noise, but not with the signal of interest (ECG) (8, 15). The estimated artifacts are subtracted from the primary noisy signal, d [ n ] = ECG + A [ n ].…”

“…For real‐time MRI where slice orientations and other imaging parameters may vary continuously and unpredictably, fixed filters are insufficient. This article will focus on signal processing techniques based on adaptive noise cancellation, which has been proposed by several groups to remove MR gradient artifacts from both ECGs (8, 9, 15, 16) and electroencephalograms (17, 18). Adaptive noise cancellation has the advantage of finding the best filter properties to remove artifacts that have overlapping spectra with the desired signal.…”

Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method

“…Singular value decomposition (SVD) [12][13][14] has also been applied in order to reduce noise in biomedical signals. One of the common approaches is the adaptive filtering (AF) architecture which has been used for interference cancellation of EEG [15][16][17][18][19] and wavelet [20][21][22][23]. In this context, principal component analysis (PCA) [24][25][26][27] and independent component analysis (ICA) [23,[28][29][30][31][32][33][34] have become popular for analysing biomedical data (e.g., EEG and EMG).…”