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  • Open Access

Computation of the gradient-induced electric field noise in 12-lead ECG traces during rapid MRI sequences

  • 1,
  • 2,
  • 1,
  • 1,
  • 3 and
  • 1
Journal of Cardiovascular Magnetic Resonance201416 (Suppl 1) :P151

https://doi.org/10.1186/1532-429X-16-S1-P151

  • Published:

Keywords

  • Magnetic Field Gradient
  • High Signal Quality
  • Balance SSFP
  • Gradient Waveform
  • System Response Function

Background

Successful physiological monitoring using a 12-lead ECG during MR imaging is essential for safe conduction of cardiovascular interventions within a MR scanner. However, ECG artifacts induced by magnetic field gradients severely affect the signal quality and fidelity. Previously, the gradient-induced artifacts were reduced by blocking ECG transmissions during all gradient ramps [1], which has been shown feasible while the method is not suitable for short-TR sequences. Theoretical and experimental studies have shown a linear relationship between electric fields and the temporal derivatives of the magnetic field gradients [2, 3]. We propose an algorithm to restore the true ECG signal by subtracting system response functions, based on the MR gradient signals, from ECG signals distorted by gradient interference.

Methods

Data Acquisition: An MRI-conditional 12-lead ECG system [1] was used to acquire data on two healthy volunteers inside a 3T MRI. Outside the MRI room, high-fidelity ECG traces, along with the x, y and z gradient waveforms were digitally recorded simultaneously at 62kHz. Balanced SSFP sequences with various slice orientations (axial, coronal, sagittal and oblique) were acquired. Data Analysis: The gradient-induced ECG noise was computed as the difference between aligned ECG traces with and without MR sequence running. The noise voltage (Vni) at each electrode (i) was modeled as a linear combination of gradient derivatives and system factors, Vni = αi•dGx/dt+βi•dGy/dt+γi•dGz/dt+Ci, where αi, βi, γi and Ci are position-dependent. These parameters were then used to reconstruct the noise, for comparison with the measured ECG noise, and to further derive the restored ECG.

Results

The recorded ECG traces and low-pass filtered gradient derivatives are displayed in Figure 1a. The computed noise vector (Vni) and the measured noise (Figure 1b) had differences of 21% ± 20% in normalized Euclidean distance. The restored ECG signal was comparable to the clean ECG segments (Figure 2), providing higher signal quality and fidelity relative to low-frequency filtering of the ECG signal. Vectorial display of the fitted parameters (Figure 3) demonstrated systematic changes across the precordial leads, and varied in magnitude between subjects.
Figure 1
Figure 1

(a): One representative ECG trace (V5) and gradient waveforms derivatives from the X, Y and Z axes during a transverse (Z, or Superior-Inferior, slice encode) SSFP acquisition, with frequency encoding along × (Right-Left) and phase encoding along Y (Anterior-Posterior). A time domain magnification (right), shows the gradient derivative signals (only 0 to 500 Hz are shown). (b): Plots of measured ECG noise (solid blue) and computed ECG noise vector (dashed red) based on the fitted parameters. There is an 18% difference between the reconstructed and measured ECG noise.

Figure 2
Figure 2

During Imaging, the restored ECG (red line) signal preserves the same signal shape as the ECG has in the absence of imaging (no gradient switching), while low-pass (LP) filtering (green dashed line) fails to clean the gradient-induced artifacts.

Figure 3
Figure 3

The fitted parameters for three SSFP acquisition orientations are listed for Subject 1 (top) and subject 2 (bottom). The 3D vector plots in the center illustrate graphically sagittal acquisitions in both subjects utilizing phase-encoding along Y (Anterior-Posterior) for the precordial electrodes V1-V6. A gradually increasing influence of the magnetic gradient fields on the ECG noise was observed from V1 to V6.

Conclusions

The gradient-derivative model closely fit the measured ECG noise, possibly allowing for efficient gradient-noise removal utilizing rapid calibration scans, combined with hardware blocking of extremely high noise intervals.

Funding

NIH U41-RR019703, R03 EB013873-01A1, and SBIR-1R43HL110427-01; AHA10SDG261039.

Authors’ Affiliations

(1)
Brigham and Women's Hospital, Boston, Massachusetts, USA
(2)
Engineering, The University of Georgia, Athens, Georgia, USA
(3)
Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA

References

  1. Tse ZT: Magn Reson Med. 2013Google Scholar
  2. Felblinger J: Magn Reson Med. 1999, 41 (4): 715-21. 10.1002/(SICI)1522-2594(199904)41:4<715::AID-MRM9>3.0.CO;2-7.View ArticlePubMedGoogle Scholar
  3. Glover PM: Phys Med Biol. 2008, 53: 361-373. 10.1088/0031-9155/53/2/005.View ArticlePubMedGoogle Scholar

Copyright

© Zhang et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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