Literature DB >> 9932340

Active cancellation system of acoustic noise in MR imaging.

C K Chen1, T D Chiueh, J H Chen.   

Abstract

In 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.

Mesh:

Year:  1999        PMID: 9932340     DOI: 10.1109/10.740881

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  10 in total

Review 1.  Acoustic noise concerns in functional magnetic resonance imaging.

Authors:  Adriaan Moelker; Peter M T Pattynama
Journal:  Hum Brain Mapp       Date:  2003-11       Impact factor: 5.038

2.  Characterization of vibration and acoustic noise in a gradient-coil insert.

Authors:  G Z Yao; C K Mechefske; B K Rutt
Journal:  MAGMA       Date:  2004-06-23       Impact factor: 2.310

3.  Extraction of overt verbal response from the acoustic noise in a functional magnetic resonance imaging scan by use of segmented active noise cancellation.

Authors:  Kwan-Jin Jung; Parikshit Prasad; Yulin Qin; John R Anderson
Journal:  Magn Reson Med       Date:  2005-03       Impact factor: 4.668

4.  Automated post-hoc noise cancellation tool for audio recordings acquired in an MRI scanner.

Authors:  Rhodri Cusack; Nick Cumming; Daniel Bor; Dennis Norris; Johannes Lyzenga
Journal:  Hum Brain Mapp       Date:  2005-04       Impact factor: 5.038

5.  In situ active control of noise in a 4 T MRI scanner.

Authors:  Mingfeng Li; Brent Rudd; Teik C Lim; Jing-Huei Lee
Journal:  J Magn Reson Imaging       Date:  2011-07-12       Impact factor: 4.813

6.  Simulation study on active noise control for a 4-T MRI scanner.

Authors:  Mingfeng Li; Teik C Lim; Jing-Huei Lee
Journal:  Magn Reson Imaging       Date:  2007-12-03       Impact factor: 2.546

7.  Music-based magnetic resonance fingerprinting to improve patient comfort during MRI examinations.

Authors:  Dan Ma; Eric Y Pierre; Yun Jiang; Mark D Schluchter; Kawin Setsompop; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2015-07-16       Impact factor: 4.668

8.  Comparison of image quality characteristics on Silent MR versus conventional MR imaging of brain lesions at 3 Tesla.

Authors:  Susanne Ohlmann-Knafo; Melanie Morlo; David Laszlo Tarnoki; Adam Domonkos Tarnoki; Barbara Grabowski; Melanie Kaspar; Dirk Pickuth
Journal:  Br J Radiol       Date:  2016-10-05       Impact factor: 3.039

9.  Convolutional auto-encoder for image denoising of ultra-low-dose CT.

Authors:  Mizuho Nishio; Chihiro Nagashima; Saori Hirabayashi; Akinori Ohnishi; Kaori Sasaki; Tomoyuki Sagawa; Masayuki Hamada; Tatsuo Yamashita
Journal:  Heliyon       Date:  2017-08-30

10.  FMRI scanner noise interaction with affective neural processes.

Authors:  Stavros Skouras; Marcus Gray; Hugo Critchley; Stefan Koelsch
Journal:  PLoS One       Date:  2013-11-18       Impact factor: 3.240

  10 in total

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