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author:

Yiman Huang (Yiman Huang.) [1] | Shuxian Qu (Shuxian Qu.) [2] | Yushu Xie (Yushu Xie.) [3] | Hanlei Wang (Hanlei Wang.) [4] | Xinlin Zhang (Xinlin Zhang.) [5] | Xiaotong Zhang (Xiaotong Zhang.) []

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Scopus

Abstract:

Objective: For low-field magnetic resonance imaging (MRI) in unshielded environment, existing methods have been proposed to eliminate electromagnetic interference (EMI) noise in each single radio-frequency (RF) receive coil. In the present study, we propose to use the EMI information from multiple MRI receive coils collectively in EMI denoising. Methods: The proposed method leverages the information of inter-channel correlation, including EMI detectors and RF receive coils to remove EMI noise. Calibration signals from both EMI detectors and RF receivers are concatenated to determine a de-correlation matrix, which is then used to denoise MRI signals. Results: Saline phantom and in vivo experiments demonstrated the efficacy of the proposed method in EMI elimination, showing that the proposed method outperformed advanced EMI elimination methods with up to 16.34% improvement in EMI noise removal percentage (NRP) and 1.58dB improvement in signal-to-noise ratio (SNR), along with reduced computational times. Conclusion: The proposed method effectively removes EMI noise and shows improved performance by using information from all receive coils. Significance: This method allows for the design of multi-receive coils for low-field MRI such as phased-arrays, which have the potential to enhance the performance in noise removal and improve the SNR in MRI signal acquisition. © 2025 IEEE.

Keyword:

denoising EMI low-field MRI signal correlation unshielded MRI

Community:

  • [ 1 ] [Huang Y.]Zhejiang University, College of Electrical Engineering, MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China
  • [ 2 ] [Qu S.]Zhejiang University, College of Biomedical Engineering & Instrument Science, MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China
  • [ 3 ] [Xie Y.]Zhejiang University, College of Electrical Engineering, MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China
  • [ 4 ] [Wang H.]Zhejiang University, College of Biomedical Engineering & Instrument Science, MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China
  • [ 5 ] [Zhang X.]Fuzhou University, College of Physics and Information Engineering, Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
  • [ 6 ] [Zhang X.]Zhejiang University, College of Electrical Engineering, the Second Affiliated Hospital of Zhejiang University School of Medicine, the State Key Lab of Brain-Machine Intelligence, MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China

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Source :

IEEE Transactions on Biomedical Engineering

ISSN: 0018-9294

Year: 2025

Issue: 7

Volume: 72

Page: 2095-2104

4 . 4 0 0

JCR@2023

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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