Electrocardiogram (ECG) acquisition is still a challenge as gradient artefacts superimposed on the electrophysiological signal can only be partially removed. The signal shape of theses artefacts can be similar to the QRS-complex, causing possible misinterpretation during patient monitoring and false triggering/gating of the MRI. For their real-time suppression, an adaptive filter is proposed. The adaptive filter is based on the noise-canceller configuration with LMS coefficient updates. The references of the noise canceller are the three gradient signals that are acquired simultaneously with the noisy ECG. Tests were done on patients, on volunteers and using an MR-safe ECG simulator. The noise canceller's performance was measured offline, simulating real-time processing by point-by-point operations. To create worst-case scenarios, clinical sequences with strong- and fast-switching gradients have been chosen. The noise-cancelling filter reduces the gradient artefacts' peak amplitudes by 80-99% after adaptation, without changing the desired ECG signal shape. The estimated reduction of total average power of the MR gradient artefacts is 62-98%. The proposed filter is capable of reducing artefacts due to strong- and fast-switching gradients in real-time applications and worst-case situations. The quality of the ECG is sufficiently high that a standard one-lead QRS-detector can be used for gating/triggering the MRI. For permanent patient monitoring, further improvements are needed.
Electrocardiogram (ECG) is required during magnetic resonance (MR) examination for monitoring patients under anaesthesia or with heart diseases and for synchronizing image acquisition with heart activity (triggering). Accurate and fast QRS detection is therefore desirable, but this task is complicated by artefacts related to the complex MR environment (high magnetic field, radio-frequency pulses and fast switching magnetic gradients). Specific signal processing has been proposed, whether using specific MR QRS detectors or ECG denoising methods. Most state-of-the-art techniques use a connection to the MR system for achieving their task, which is a major drawback since access to the MR system is often restricted. This paper introduces a new method for on-line ECG signal enhancement, called ICARE, which takes advantage of using multi-lead ECG and does not require any connection to the MR system. It is based on independent component analysis (ICA) and applied in real time. This algorithm yields accurate QRS detection for efficient triggering.
ECGs are currently acquired during magnetic resonance examinations. This "hostile" environment highly distorts ECG signals, due to the high-static magnetic field, RF pulses and fast switching magnetic gradients. Specific signal processing is then required since the ECG signal is used for image synchronization with heart activity (or triggering) and for patient monitoring. A new set of two magnetic field gradient (MFG) artifact reduction methods, based on ECG and MFG artifact modelings and Bayesian filtering, is herein presented and will be called Bayesian gradient artifact reduction monitoring (BAGARRE-M) and BAGARRE-triggering. These algorithms overcome the limitations of state-of-the-art methods and enable accurate processing of very noisy ECG acquisitions during MRI. Whether for triggering or monitoring purposes, the presented methods overcome state-of-the-art techniques with both better QRS detection accuracy and signal denoising quality.
Automatic Electrocardiogram (ECG) analysis, especially QRS detection, is still a challenging task. This is even more the case when ECG is acquired during Magnetic Resonance (MR) examination. The MR environment highly distorts ECG, with Hall Effect, due to the important static magnetic field, and artifacts, caused by fast switching magnetic field gradients. Detection of QRS complexes is then affected. In this paper, a new specific MR QRS detector is presented. This method is based on the modulus maximum lines and on the Lipschitz exponent estimation they offer. The use of this regularity characterization enables to distinguish between QRS complexes and MR artifacts. This detector outperforms existing algorithms with almost 99% sensitivity and positive prediction value.
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