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Reference signal extraction from corrupted ECG using wavelet decomposition for MRI sequence triggering: application

Dima Abi-Abdallah1, Eric Chauvet, Latifa Bouchet-Fakri

  • 1Laboratoire de Biomécanique et Génie Biomédical, UMR CNRS 6600, Université de Technologie de Compiègne, France. dima.abiabdallah@utc.fr

Biomedical Engineering Online
|March 1, 2006
PubMed
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This study presents a wavelet-based method to extract clean electrocardiogram (ECG) signals from noisy Magnetic Resonance Imaging (MRI) data. The technique effectively synchronizes cardiac imaging by filtering out MRI artifacts, improving image quality.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Imaging

Background:

  • Nuclear Magnetic Resonance (NMR) imaging requires high spatial and temporal resolution.
  • High-gradient MRI generates noise that interferes with electrophysiological signals used for synchronization.
  • Processing these contaminated signals is crucial for accurate cardiac imaging.

Purpose of the Study:

  • To extract an efficient reference signal from electrocardiogram (ECG) contaminated by Magnetic Resonance Imaging (MRI) artifacts.
  • To develop a method for image triggering and cardiac rhythm monitoring during MRI examinations.

Main Methods:

  • Sub-band decomposition using wavelet filters applied to ECG signals recorded during Gradient Echo (GE), Fast Spin Echo (FSE), and Inversion Recovery with Spin Echo (IRSE) sequences.

Related Experiment Videos

  • Analysis of noise generated by each imaging sequence to select optimal wavelet functions (Daubechies, Coiflets, Symlets).
  • Decomposition of ECG signals into 8 scales, combining sub-bands containing QRS energy to reconstruct a reference signal.

    • Main Results:

    • The wavelet-based method demonstrated satisfactory performance on simulated and actual ECG records, even with low signal-to-noise ratios (SNR < -5 dB).
    • Specific wavelet functions showed varying efficiencies depending on the MRI sequence: coif5 for GE, sym8 for FSE, and sym4 for IRSE.
    • Effective synchronization was achieved despite significant noise contamination.

    Conclusions:

    • Sub-band decomposition is highly effective for extracting reference ECG signals from corrupted data for MRI triggering.
    • Selecting appropriate wavelet functions tailored to specific MRI sequences significantly enhances reference signal quality and image synchronization.