MRI Acoustic Noise Prediction and Silent Sequence Optimization

Abstract:
The large gradient switching-induced Lorentz forces can cause considerable acoustic noise, which is a disadvantage of magnetic resonance scanning, causing patient restlessness and safety risks. This sound should be characterized, predicted, evaluated, and reduced. In this study, the accurate gradient-to-acoustic noise transfer functions of x, y, and z axes gradient coils are measured with white noise input. Based on it, a 4th-order polynomial gradient smoothing function is proposed to reduce high-frequency components, and a genetic algorithm is employed to further optimize parameters to avoid resonance with the scanner. The example 2D gradient recalled echo sequence sound is decreased by 25.28 dBA (94.55%) and image quality is maintained with a negligible difference. This can be applied to various sequences to improve scanning comfort and reduce hearing harm.