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Optimal parameter selection of PRiME adaptive noise cancellation for reduction of MRI interference on electrocardiography signals

Thursday, September 13, 2018 — Poster Session IV

3:30 p.m. – 5:00 p.m.
FAES Terrace
CIT
BIOENG-4

Authors

  • JW Kakareka
  • T Arginteanu
  • DA Herzka
  • RH Pursley
  • DR McGuirt
  • RJ Lederman
  • TJ Pohida

Abstract

The rapidly varying magnetic field gradients of magnetic resonance imaging (MRI) systems generate noise in electronics and cabling which cause errors in the measurement of physiological signals such as electrocardiograms (ECG). PRiME (Physiological Recording in an MRI Environment) was previously developed for recording ECG and invasive blood pressure (IBP) signals during MRI-guided cardiovascular intervention procedures. A vital component of the PRiME system is a Least-Mean-Squares (LMS) adaptive filter which attenuates noise induced by the MRI gradient fields. The adaptive filter uses the three magnetic gradient field generator control signals as error inputs to the algorithm, which are used to adaptively reduce the noise in the acquired ECG signal. The two main parameters of the LMS adaptive filter are filter length (i.e., the number of taps and weights) and the adaptation step size beta. Currently, in the PRiME system, the filter length is fixed at 100 taps, and the beta parameter is user-selected from 3 pre-determined values. These beta values and the filter length were empirically determined during initial testing as performing adequately over a wide range of animal and human subjects. However, these filter parameters are not optimal for any given subject, leading to reduced performance for the sake of ease-of-use. Previous work evaluated combining the gradients waveforms into a single filter input signal and finding optimal beta values with a fixed filter length. Here, to further improve performance of the adaptive filter, we evaluate the effects of different filter lengths on ECG signal fidelity and overall noise reduction.

Category: Biomedical Engineering and Biophysics