Casper K. ChenTZI-DAR CHIUEHJYH-HORNG CHEN2018-09-102018-09-101999-0100189294http://scholars.lib.ntu.edu.tw/handle/123456789/352042In this paper, we introduce a new neural-network architecture for reducing the acoustic noise level in magnetic resonance (MR) imaging processes. The proposed neural network (NN) consists of two cascaded time- delay NN's (TDNN's). This NN is used as the predictor of a feedback active noise control (ANC) system for reducing acoustic noises. Experimental results with real MR noises show that the proposed system achieved an average noise power attenuation of 18.75 dB, which compares favorably with previous studies. Preliminary results also show that with the proposed ANC system installed, acoustic MR noises are greatly attenuated while verbal communication during MRI sessions is not affected.Active noise cancellation; Backpropagation algorithm; Magnetic resonance (MR); Multilayer perceptrons; Time-delay neural networks (TDNN's)article; artificial neural network; feedback system; image processing; nuclear magnetic resonance imaging; signal noise ratio; signal processingjournal article10.1109/10.74088199323402-s2.0-0032956114WOS:000078211900008