Abstract
The electrocardiogram (ECG) is the state-of-the-art signal for patient monitoring and gating in cardiovascular magnetic resonance (CMR) imaging applications. However, ECG signals are severely distorted during MRI scans due to the effects of static magnetic fields, radio frequency pulses and fast-switching gradient magnetic fields. Gradient-induced artifacts that cause high frequency peaks in the ECG signal especially hamper a correct and reliable QRS detection. To cope with this problem, a new median-based real-time gradient filter (M1) approach was developed. To improve the filter results, a preprocessing step based on higher-order statistics (M2) was added to this. For the evaluation of the filtering techniques, ECG signals were acquired in a 3T MRI scanner during different MR sequences. A qualitative comparison was made using the mean square error as well as the signal power before and after filtering and the results of the QRS detection. Here, reliable results were achieved (detection error rate [DER] M1: 0.23%, DER M2: 0.74%). It was shown that the two developed techniques allowed a reliable suppression of the gradient artifacts in real time.
Author Statement
Research funding: Bundesministerium für Wirtschaft und Technologie, (Grant/Award Number: “KF3172301JL3”).
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent has been obtained from all individuals.
Ethical approval: With reference to the ECG recording, the database complies with all the relevant national regulations and institutional policies, is in accordance with the tenets of the Helsinki Declaration and has been approved by the local Ethics Committee. To guarantee patient safety, only MR-safe equipment was used.
References
[1] Abächerli R, Hornaff S, Leber R, Schmid HJ, Felblinger J. Improving automatic analysis of the electrocardiogram acquired during magnetic resonance imaging using magnetic field gradient artefact suppression. J Electrocardiol 2006; 39: 134–139.10.1016/j.jelectrocard.2006.05.028Search in Google Scholar
[2] Abächerli R, Pasquier C, Odille F, Felblinger J. Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time Filter with lms coeffcient updates. J Magn Reson Imaging 2005; 18: 41–50.Search in Google Scholar
[3] Abächerli R, Pasquier C, Odille F, Kraemer M, Schmid JJ, Felblinger J. Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time filter with LMS coefficient updates. Eur Soc For Magn Reson Med Biol 2005; 18: 41–50.10.1007/s10334-004-0093-1Search in Google Scholar
[4] Abi-Abdallah D, Chauvet E, Bouchet-Fakri L, Bataillard A, Briguet A, Fokapu O. Reference signal extraction from corrupted ECG using wavelet decomposition for MRI sequence triggering: application to small animals. BioMed Eng Online 2006; 5: 11.10.1186/1475-925X-5-11Search in Google Scholar
[5] Abi-Abdallah D, Drochon A, Robin V, Fokapu O. Cardiac and respiratory MRI gating using combined wavelet sub-band decomposition and adaptive filtering. Ann Biomed Eng 2007; 35: 733–743.10.1007/s10439-007-9285-ySearch in Google Scholar
[6] Abi-Abdallah D, Drochon A, Robin V, Poulet P, Fokapu O. Removing the MHD Artifacts from the ECG signal for cardiac MRI synchronization. In: Proceedings of the 3rd European Medical & Biological Engineering Conference (2005).Search in Google Scholar
[7] Chia JM, Fischer SE, Wickline SA, Lorenz CH. Performance of QRS detection for cardiac magnetic resonance imaging with a noval vectorcardiographic triggering method. J Magn Reson Imaging 2000; 12: 678–688.10.1002/1522-2586(200011)12:5<678::AID-JMRI4>3.0.CO;2-5Search in Google Scholar
[8] Crawford, MH. ACC/AHA guidelines for ambulatory electrocardiography. J Am Coll Cardiol 1999; 34: 912–948.10.1016/S0735-1097(99)00354-XSearch in Google Scholar
[9] Dale BM, Brown MA, Semelka RC. MRI – Basics, Principles and Applications, 5th ed. Wiley Blackwell, Chichester, West Sussex, UK, 2015.10.1002/9781119013068Search in Google Scholar
[10] Felblinger J, Lehmann C, Boesch C. Electrocardiogram acquisition during MR examinations for patients monitoring and sequence triggering. Magn Reson Med 1994; 32: 523–529.10.1002/mrm.1910320416Search in Google Scholar
[11] Felblinger J, Slotboom J, Kreis R, Jung B, Boesch C. Restoration of electrophysiological signals distorted by inductive effects of magnetic field gradients during MR sequences. Magn Reson Med 1999; 41: 715–721.10.1002/(SICI)1522-2594(199904)41:4<715::AID-MRM9>3.0.CO;2-7Search in Google Scholar
[12] Gierstorfer, Andreas B. Optimierung eines magnetresonanztomographietauglichen EKG-Filters. München, Bayern, Deutschland Hochschule für angewandte Wissenschaften – FH München, Bachelorthesis, 2009.Search in Google Scholar
[13] Huber PJ. Projection persuit. Ann Statist 1985; 13: 435–475.10.1214/aos/1176349519Search in Google Scholar
[14] International Organization for Standardization: Medical electrical equipment - Particular requirements for the basic safety and essential performance of electrocardiographic monitoring equipment IEC. Standard, 2007.Search in Google Scholar
[15] Kinouchi Y, Yamaguchi H, Tenforde T. Theoretical analysis of magnetic field interactions with aortic blood flow. Bioelectromagnetics 1996; 17: 21–32.10.1002/(SICI)1521-186X(1996)17:1<21::AID-BEM3>3.0.CO;2-8Search in Google Scholar
[16] Krug JW, Clifford GD, Rose GH, Oster J. The limited applicability of Wiener filtering to ECG signals disturbed by the MHD effect. Eur Signal Process Conf 2012; 20: 959–963.Search in Google Scholar
[17] Krug JW, Rose GH, Stucht D, Clifford GD, Oster J. Filtering the magnetohydrodynamic effect from 12-lead ECG signals using independent component analysis. Comput In Cardiol 2012; 39: 589–592.Search in Google Scholar
[18] Laudon MK, Webster JG, Frayne R, Grist TM. Minimizing interference from magnetic resonance imagers during electrocardiography. IEEE Trans Biomed Eng 1998; 45: 160–164.10.1109/10.661264Search in Google Scholar
[19] Lee J, Sagel S, Stanley R, Heiken J. Computed body tomography with MRI correlation. Lippincott Williams & Wilkins, Philidelphia, Pennsylvenia, USA, 2006; 1.Search in Google Scholar
[20] Moore JC, Tulsa O. Method of improving the quality of an electrocardiogram obtained from a patient undergoing magnetic resonance imaging. US Patent 1991; 4991580.10.1016/0730-725X(92)90537-ASearch in Google Scholar
[21] Mossawir BJ, Venook RD, Wang CC. On the applicability of the LMS algorithm to gradient noise elimination from EKG signals during an MRI scan. Stanford University, CA, 2005.Search in Google Scholar
[22] Nijm GM, Swiryn S, Larson AC, Sahakian AV. A 3D model of magnetohydrodynamic voltages: comparison with voltages observed on the surface ECG during cardiac MRI. Comput In Cardiol 2007; 34: 45–48.10.1109/CIC.2007.4745417Search in Google Scholar
[23] Nijm GM, Swiryn S, Larson AC, Sahakian AV. Extraction of the magnetohydrodynamic blood flow potential from the surface electrocardiogram in magnetic resonance imaging. Med Biol Eng Comput. 2008; 46: 729–733.10.1007/s11517-008-0307-1Search in Google Scholar PubMed
[24] Odille F, Pasquier C, Roger A, Vuissoz PA, Zientara GP, Felblinger J. Noise cancellation signal processing method and computer system for improved real-time electrocardiogram artifact correction during MRI data acquisition. IEEE T Biomed Eng 2007; 54: 630–640.10.1109/TBME.2006.889174Search in Google Scholar PubMed
[25] Oster J, Geist M, Pietquin O, Clifford GD. Filtering the pathological ventricular rhythms cduring MRI scanning. Int J Bioelectromagnetism 2013; 15: 54–59.Search in Google Scholar
[26] Oster J, Geist M, Tse Z, Schmidt EJ, Pietquin O, Clifford GD. Non-linear Bayesian suppression of magnetohydrodynamic effect for accurate electrocardiogram analysis during MRI. Int Soc Magn Reson Med 2013; 21: 4321.Search in Google Scholar
[27] Oster J, Pietquin O, Abächerli R, Kraemer M, Felblinger J. A specific QRS detector for electrocardiography during MRI: using wavelets and local regularity characterization. Int Conf Acoust Speech Signal Process 2009; 34: 341–344.10.1109/ICASSP.2009.4959590Search in Google Scholar
[28] Oster J, Pietquin O, Kraemer M, Felblinger J. Bayesian framework for artifact reduction on ECG in MRI. ICASSP 2010; 35: 489–492.10.1109/ICASSP.2010.5495684Search in Google Scholar
[29] Oster J, Pietquin O, Kraemer M, Felblinger J. Magnetic field gradient artifact reduction on ECG for improved triggering. Int Soc Magn Reson Med 2010; 18: 5009.Search in Google Scholar
[30] Oster J, Pietquin O, Kraemer M, Felblinger J. Nonlinear Bayesian filtering for denoising of electrocardiograms acquired in a magnetic resonance environment. IEEE T Biomed Eng 2010; 57: 1628–1638.10.1109/TBME.2010.2046324Search in Google Scholar PubMed
[31] Oster J, Pietquin O, Roger A, Kraemer M, Felblinger J. Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering. Physiolog Meas 2009; 30: 1381–1397.10.1088/0967-3334/30/12/007Search in Google Scholar PubMed
[32] Park HD, Cho SP, Lee KJ. A method for generating MRI cardiac and respiratory gating pulse simultaneously based on adaptive real-time digital filters. Comput In Cardiol 2006; 33: 813–816.Search in Google Scholar
[33] Park HD, Jang BR, Cho SP, Kim HJ, Choi KH, Lee KJ. Minimizing MR gradient and RF pulse artefacts on ECG signals for MRI gating based on an adaptive real-time digital filter. Int Fed Med Biologic Eng 2007; 14: 1127–1130.10.1007/978-3-540-36841-0_270Search in Google Scholar
[34] Park H, Park Y, Cho S, Jang B, Lee K. New cardiac MRI gating method using event-synchronous adaptive digital filter. Ann Biomed Eng 2009; 37: 2170–2187.10.1007/s10439-009-9764-4Search in Google Scholar
[35] Qui G. Functional optimization properties of median filtering. In: IEEE Signal Process Lett 1994; 1: Nr. 4, S. 64–65.10.1109/97.295334Search in Google Scholar
[36] Sansone M, Mirarchi L, Bracale M. Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLF filtering. Med Biol Eng Comput 2010; 48: 475–482.10.1007/s11517-010-0596-zSearch in Google Scholar
[37] Schaefer DJ, Bourland JD, Nyenhuis JA. Review of patient safety in time-varying gradient fields. J Magn Reson Imaging 2000; 12: 20–29.10.1002/1522-2586(200007)12:1<20::AID-JMRI3>3.0.CO;2-YSearch in Google Scholar
[38] Schmidt M. Statistische Methoden zur Filterung und Analyse von EKG-Signalen während der Magnetresonanztomographie. Magdeburg, Deutschland, Otto-von-Guericke-Universität, Dissertation, 2017.Search in Google Scholar
[39] Schmidt M, Krug JW, Gierstorfer A, Rose G. QRS detection using 5th order cumulants for ECG gated cardiac MRI. BMT 2014; 59: 180.Search in Google Scholar
[40] Schmidt M, Krug JW, Gierstorfer A, Rose G: A real-time QRS detector based on higher-order statistics for ECG gated cardiac MRI. In: Comput In Cardiol 2014; 41: 1,733–736.Search in Google Scholar
[41] Schmidt M, Krug JW, Rose G. A real-time QRS detector based on higher-order statistics for ECG gated cardiac MRI. Comput In Cardiol 2014; 41: 733–736.Search in Google Scholar
[42] Schmidt M, Schumann A, Krug JW, Bär K-J, Rose G. Estimation of a respiratory signal from a single-lead ECG using 4th order central moments. Biomed Tech 2015; 49: 61–64.10.1515/cdbme-2015-0016Search in Google Scholar
[43] Schmitt F. The gradient system. Understanding gradients from an EM perspective: (gradient linearity, eddy currents, maxwell terms & peripheral nerve stimulation). Int Soc Magn Reson Med 2013; 21: 159–172.Search in Google Scholar
[44] Scott AD, Keegan J, Firmin DN. Motion in cardiovascular MR imaging. Radiology 2009; 250: 331–351.10.1148/radiol.2502071998Search in Google Scholar
[45] Stranneby D, Walker W. Digital signal processing and applications. Newnes, Elsevier, Oxford, UK, 2004; 2.10.1016/B978-075066344-1/50009-4Search in Google Scholar
[46] Wendt RE, Rokey R, Vick GW, Johnston DL. Electrocardiographic gating and monitoring in NMR imaging. Magn Reson Imaging 1988; 6: 89–95.10.1016/0730-725X(88)90528-0Search in Google Scholar
[47] Wu V, Barbash IM, Ratnayaka K, et al. Adaptive noise cancellation to suppress electrocardiography artifacts during real-time interventional MRI. J Magn Reson Imaging 2011; 33: 1184–1193.10.1002/jmri.22530Search in Google Scholar
[48] Zhang SH, Tse ZT, Dumoulin CL, et al. Gradient-induced voltages on 12-lead ECGs during high duty-cycle MRI sequences and a method for their removal considering linear and concomitant gradient terms. Magn Reson Med 2016; 75: 2204–2216.10.1002/mrm.25810Search in Google Scholar
©2018 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research articles
- A new in vitro spine test rig to track multiple vertebral motions under physiological conditions
- In-service characterization of a polymer wick-based quasi-dry electrode for rapid pasteless electroencephalography
- Spike detection using a multiresolution entropy based method
- Obstacles in using a computer screen for steady-state visually evoked potential stimulation
- Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine
- Filtering of ECG signals distorted by magnetic field gradients during MRI using non-linear filters and higher-order statistics
- Failure analysis of eleven Gates Glidden drills that fractured intraorally during post space preparation. A retrieval analysis study
- Assessing multiple muscle activation during squat movements with different loading conditions – an EMG study
- In-vivo monitoring of infection via implantable microsensors: a pilot study
- Analysis of structural brain MRI and multi-parameter classification for Alzheimer’s disease
- False spectra formation in the differential two-channel scheme of the laser Doppler flowmeter
- A priori knowledge integration for the detection of cerebral aneurysm
- Is the location of the signal intensity weighted centroid a reliable measurement of fluid displacement within the disc?
- Image-based 3D surface approximation of the bladder using structure-from-motion for enhanced cystoscopy based on phantom data
- Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization
- Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm
- A hybrid active force control of a lower limb exoskeleton for gait rehabilitation
- Short communication
- Can somatosensory electrical stimulation relieve spasticity in post-stroke patients? A TMS pilot study