BackgroundIn Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images.MethodsA strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG.ResultsECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved.Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively.ConclusionsThe presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.

For a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.

The electrocardiogramm (ECG) is the state-of-the-art signal for gating in cardiovascular magnetic resonance imaging and patient monitoring. Using the ECG for gating and monitoring during the magnetic resonance imaging examination is a high challenging task due to the superimposition of the magnetohydrodynamic effect, radio-frequency (RF) pulses and fast switching gradient magnetic fields. The gradient induced artifacts hamper the correct QRS detection which is needed for correct gating and heart rate calculation and ECG displaying for patient monitoring. To suppress the gradient artifacts from the ECG signal acquired during MRI, a technique based on the Wilcoxon filter was developed. It was evaluated using ECG signals of 14 different subjects acquired in a 3 T MRI scanner. It could be shown reliable results for reducing gradient induced artifacts in the ECG signal in real-time.

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.

Magnetic resonance imaging (MRI) is an imaging technique widely used in medical diagnostics as well as in minimally invasive, image guided interventions. During an MRI scan, the patient's electrocardiogram (ECG) can be required for either gating or patient monitoring purposes. However, the hostile environment of an MRI scanner with its various types of magnetic fields (strong static magnetic field, switch gradient magnetic fields in the Hz or kHz range as well as high frequency magnetic fields in the MHz range) causes serve distortions of the acquired ECG signals. These distortions or artifacts hamper the QRS detection and a more detailed ECG-based diagnosis. To study these effects of the MRI environment, we created a database consisting of ECG signals acquired in various MRI scanners. This database was published on Physionet in order to encourage other researchers to enhance the quality of ECG signals during MRI exams.