Adaptive noise cancellation to suppress electrocardiography artifacts during real-time interventional MRI.

Published

Journal Article

PURPOSE:To develop a system for artifact suppression in electrocardiogram (ECG) recordings obtained during interventional real-time magnetic resonance imaging (MRI). MATERIALS AND METHODS:We characterized ECG artifacts due to radiofrequency pulses and gradient switching during MRI in terms of frequency content. A combination of analog filters and digital least mean squares adaptive filters were used to filter the ECG during in vivo experiments and the results were compared with those obtained with simple low-pass filtering. The system performance was evaluated in terms of artifact suppression and ability to identify arrhythmias during real-time MRI. RESULTS:Analog filters were able to suppress artifacts from high-frequency radiofrequency pulses and gradient switching. The remaining pulse artifacts caused by intermittent preparation sequences or spoiler gradients required adaptive filtering because their bandwidth overlapped with that of the ECG. Using analog and adaptive filtering, a mean improvement of 38 dB (n = 11, peak QRS signal to pulse artifact noise) was achieved. This filtering system was successful in removing pulse artifacts that obscured arrhythmias such as premature ventricular complexes and complete atrioventricular block. CONCLUSION:We have developed an online ECG monitoring system employing digital adaptive filters that enables the identification of cardiac arrhythmias during real-time MRI-guided interventions.

Full Text

Cited Authors

  • Wu, V; Barbash, IM; Ratnayaka, K; Saikus, CE; Sonmez, M; Kocaturk, O; Lederman, RJ; Faranesh, AZ

Published Date

  • May 2011

Published In

Volume / Issue

  • 33 / 5

Start / End Page

  • 1184 - 1193

PubMed ID

  • 21509878

Pubmed Central ID

  • 21509878

Electronic International Standard Serial Number (EISSN)

  • 1522-2586

International Standard Serial Number (ISSN)

  • 1053-1807

Digital Object Identifier (DOI)

  • 10.1002/jmri.22530

Language

  • eng