Skip to main content

Advertisement

SpringerLink
Log in

Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLS filtering

  • Technical Note
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

One of the main vital signs used in patient monitoring during Magnetic Resonance Imaging (MRI) is Electro-Cardio-Gram (ECG). Unfortunately, magnetic fields gradients induce artefacts which severely affect ECG quality. Adaptive Noise Cancelling (ANC) is one of the preferred techniques for artefact removal. ANC involves the adaptive estimation of the impulse response of the system constituted by the MRI equipment, the patient and the ECG recording device. Least Mean Square (LMS) adaptive filtering has been traditionally employed because of its simplicity: anyway, it requires the choice of a step-size parameter, whose proper value for the specific application must be estimated case by case: an improper choice could yield slow convergence and unsatisfactory behaviour. Recursive Least Square (RLS) algorithm has, potentially, faster convergence while not requiring any parameter. As far as the authors’ knowledge, there is no systematic analysis of performances of RLS in this scenario. In this study we evaluated the performance of RLS for adaptive removal of artefacts induced by magnetic field gradients on ECG in MRI, in terms of efficacy of suppression. Tests have been made on real signals, acquired via an expressly developed system. A comparison with LMS was made on the basis of opportune performance indices. Results indicate that RLS is superior to LMS in several respects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Abächerli R, Pasquier C, Odille F, Kraemer M, Schmid JJ, Felblinger J (2005) Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time filter with LMS coefficient updates. Magn Reson Mater Phys Biol Med 18(1):41–50

    Google Scholar 

  2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 6:716–723

    Article  MathSciNet  Google Scholar 

  3. Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12:230–239

    Article  Google Scholar 

  4. Bouchard M, Quednau S (2000) Multichannel recursive-least-squares algorithms and fast transversal filter algorithms for active noise control and sound reproduction systems. IEEE Trans Acoust Speech Signal Process 5(8):606–618

    Google Scholar 

  5. Chia JM, Fischer SE, Wickline SA, Lorenz CH (2000) Performance of QRS detection for cardiac magnetic resonance imaging with a novel vectorcardiographic triggering method. J Magn Reson Imaging 12(5):678–688

    Article  Google Scholar 

  6. Felblinger J, Slotboom J, Kreis R, Jung B, Boesch C (1999) Restoration of electrophysiological signals distorted by inductive effects of magnetic field gradients during MR sequences. Magn Reson Med 41(4):715–721

    Article  Google Scholar 

  7. Finn JP, Nael K, Deshpande V, Ratib O, Laub G (2006) Cardiac MR imaging: state of the technology. Radiology 241(2):338–354

    Article  Google Scholar 

  8. Gimbel JR, Johnson D, Levine PA, Wilkoff BL (1996) Safe performance of magnetic resonance imaging on five patients with permanent cardiac pacemakers. Pacing Clin Electrophysiol 19(6):913–919

    Article  Google Scholar 

  9. Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahana P, David O (2007) A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage 38:124–137

    Article  Google Scholar 

  10. Hayes MH (1996) Statistical digital signal processing and modelling. Wiley, New York

    Google Scholar 

  11. Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice Hall International Edition

  12. Huber S, Muthupillai R, Cheong B, Wible JH Jr, Shah D, Woodard P, Grothues F, Mahrholdt H, Rochitte CE, Masoli O, Kim RJ, Schwaiger CM, Fuisz A, Kramer C, van Rossum AC, Biederman R, Lombardi M, Martin E, Kevorkian R, Flamm SD (2008) Safety of gadoversetamide in patients with acute and chronic myocardial infarction. J Magn Reson Imaging 28(6):1368–1378

    Article  Google Scholar 

  13. Kanal E, Shellock FG (1992) Patient monitoring during clinical MR imaging. Radiology 185:623–629

    Google Scholar 

  14. Kettenbach J, Kacher DF, Koskinen SK, Silverman SG, Nabavi A, Gering D, Tempany CM, Schwartz RB, Kikinis R, Black PM, Jolesz FA (2000) Interventional and intraoperative magnetic resonance imaging. Annu Rev Biomed Eng 2:661–690

    Article  Google Scholar 

  15. Kligfield P, Gettes LS, Bailey JJ, Childers R, Deal BJ, Hancock EW, van Herpen G, Kors JA, Macfarlane P, Mirvis DM, Pahlm O, Rautaharju P, Wagner GS, Josephson M, Mason JW, Okin P, Surawicz B, Wellens H (2007) Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation 115(10):1306–1324

    Google Scholar 

  16. Kugel H, Bremer C, Püschel M, Fischbach R, Lenzen H, Tombach B, Van Aken H, Heindel W (2003) Hazardous situation in the MR bore: induction in ECG leads causes fire. Eur J Radiol 13(4):690–694

    Google Scholar 

  17. Laudon MK, Webster JG, Frayne R, Grist TM (1998) Minimizing interference from magnetic resonance imagers during electrocardiography. IEEE Trans Biomed Eng 45(2):160–164

    Article  Google Scholar 

  18. Macchi O, Eweda E (1983) Second-order convergence analysis of stochastic adaptive linear filtering. IEEE Trans Automat Contr 1:76–85

    Article  MathSciNet  Google Scholar 

  19. Masterton RAJ, Abbott DF, Fleming SW, Jackson GD (2007) Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings. Neuroimage 37:202–211

    Article  Google Scholar 

  20. Niazy RK, Beckmann CF, Iannetti GF, Brady JM, Smith SM (2005) Removal of fMRI environment artifacts form EEG data using optimal basis sets. Neuroimage 28:720–737

    Article  Google Scholar 

  21. Nijm GM, Swiryn S, Larson AC, Sahakian AV (2008) Extraction of the magnetohydrodynamic blood flow potential from the surface electrocardiogram in magnetic resonance imaging. Med Biol Eng Comput 46(7):729–733

    Article  Google Scholar 

  22. Odille F, Pasquier C, Abächerli R, Vuissoz PA, Zientara GP, Felblinger J (2007) Noise cancellation signal processing method and computer system for improved real-time electrocardiogram artifact correction during MRI data acquisition. IEEE Trans Biomed Eng 54(4):630–640

    Article  Google Scholar 

  23. Park HD, Cho SP, Lee KJ, Park YC (2007) Minimisation of gradient artefacts in cardiac-MRI-gating using adaptive interference cancellation filter with synthesised reference. Electron Lett 43(20):1070–1071

    Article  Google Scholar 

  24. Puthusserypady S (2007) Extraction of fetal electrocardiogram using H(infinity) adaptive algorithms. Med Biol Eng Comput 45(10):927–937

    Article  Google Scholar 

  25. Rissanen J (1978) Modeling by shortest data description. Automatica 14:465–471

    Article  MATH  Google Scholar 

  26. Schaefer DJ, Bourland JD, Nyenhuis JA (2000) Review of patient safety in time-varying gradient fields. J Magn Reson Imaging 12:20–29

    Article  Google Scholar 

  27. Sijbers J, Michiels I, Verhoye M, Van Audekerke J, Van Der Linden A, Van Dyck D (1999) Restoration of MR-induced artefacts in simultaneously recorded MR/EEG data. Magn Reson Imaging 17(9):1383–1391

    Article  Google Scholar 

  28. Tate CN, Goodyear CC (1983) Note on the convergence of linear predictive filters, adapted using the LMS algorithm. Electron Circ Syst IEE Proc G 130(2):61–64

    Google Scholar 

  29. Tenforde TS (2005) Magnetically induced electric fields and currents in the circulatory system. Prog Biophys Mol Biol 87(2–3):279–288

    Article  Google Scholar 

  30. Vaidyanathan PP (1985) Optimal design of linear phase FIR digital filters with very flat passband and equiripple stopbands. IEEE Trans Circuits Syst CAS-32:904–907

    Article  Google Scholar 

  31. Westbrook C (1999) Handbook of MRI techniques. Paperback

  32. Widrow B, Glover JR, McCool JM (1975) Adaptive noise cancelling: principles and applications. Proc IEEE 63(12):1692–1716

    Article  Google Scholar 

  33. Yang B (1994) A note on the error propagation analysis of recursive least squares algorithm. IEEE Trans Signal Process 42:3523–3525

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Dr. Andrea Grieco for technical support in ECG and gradient signal acquisition. Moreover, we are grateful to MD Alfredo Siani at Institute for Cancer Research “Pascale” in Naples for providing all the required authorisations. Moreover we would like to thank the editor and the three anonymous reviewers whose constructive comments contributed to significantly improve the quality of the paper.

Author information

Authors and Affiliations

  1. Department of Biomedical, Electronic and Telecommunications Engineering, University “Federico II” of Naples, via Claudio 21, 80131, Naples, Italy

    Mario Sansone & Marcello Bracale

  2. Siemens S.p.A, Healthcare Sector, Customer Services, Via Francia, 12, 37135, Verona, Italy

    Luciano Mirarchi

Authors
  1. Mario Sansone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luciano Mirarchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcello Bracale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Sansone.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sansone, M., Mirarchi, L. & Bracale, M. Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLS filtering. Med Biol Eng Comput 48, 475–482 (2010). https://doi.org/10.1007/s11517-010-0596-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0596-z

Keywords

Search

Navigation