Experimental Evaluation of Adaptive Step-Size Based FxLMS Approach for Active Noise Control in Magnetic Resonance Imaging Systems

Abstract

Magnetic Resonance Imaging (MRI) scanners emit up to 135 decibels of acoustic noise, which is a major source of discomfort for patients and personnel evaluating them during routine medical scans, necessitating the development of a method to reduce the acoustic noise generated during MRI testing. The goal of this study is to propose a frequency-domain Active Noise Control (ANC) method for acoustic noise reduction in MRI and to demonstrate its ANC effectiveness on an experimental MRI scanner model specifically built for this purpose. In comparison to the standard Least Mean Square (LMS) algorithm, we used the Filtered-x Least Mean Square (FxLMS) approach with an adaptive variable step-size approach to adjust the filter coefficients dynamically, which considerably enhances the ANC system's convergence and reduces acoustic noise. The simulation results obtained from the MATLAB Simulink model on a pre-recorded 30-sec MRI noise signal represented by the step-size variation over time, error and noise convergence plots reveal that the adaptive step-size FxLMS (ASFxLMS) technique increases noise and error convergence rate significantly more than existing ANC algorithms to facilitate its use during MRI scans. Experimental results with our functional MRI (fMRI) testbed show approximately 25-dB overall noise reduction relative to the noise levels without ANC.