MRI acoustic noise reduction using CNN

Presented in this paper is a revolutionary deep learning-based architecture for reducing the noise generated during Magnetic Resonance Imaging (MRI) scans. The proposed differs from usual adaptive algorithms used Active Noise Control (ANC). In present work, we are exploring use of Deep Convolutional Artificial Networks to recognize advanced sounds. By applying DL-NN 513-time segment, 180-degree phase shift sample generated. After computational simulation analyses were performed, experimental results show that performance average power can be reduced by approximately 10 15 dB.