MagnetoHemoDynamics Effect on Electrocardiograms. In: Metaxas DN, Axel L, editors. Functional Imaging and Modeling of the Heart. Berlin: Springer Berlin Heidelberg; 2011. pp. 325-32. [DOI: 10.1007/978-3-642-21028-0_42]

Acquisition of electrocardiogram signals during magnetic resonance imaging

The recording of the electrocardiogram (ECG) during magnetic resonance imaging (MRI) acquisition is of great interest and importance. Firstly, MRI acquisition is a relatively slow process, which therefore complicates the imaging of moving organs. Cardiac MRI requires the development of strategies for acquiring high quality images, which is mainly achieved by synchronising the image acquisition with a specific time during the cardiac cycle. The ECG is used to monitor the heart's activity, and the detection of the largest and steepest peak in the cardiac cycle (the QRS complex) triggers the acquisition of slices of the k-space. Secondly, patients undergoing an MRI examination need to be monitored for safety during the procedure, and therefore ECG signals are used to track their cardiovascular state in real time. However, there are significant barriers to the accurate observation and processing of the ECG during MRI acquisition. In particular, the flow of charged blood particles through the large applied magnetic field leads to an extra current source, known as the magnetohdrodymanic (MHD) effect. This review article discusses these barriers and state-of-the-art solutions. An overview of the relevant technology including hardware and applications are described. The development of new software tools for the processing of the ECG signals acquired during MRI is also detailed. These developments include the design of specific QRS detection algorithms, which are able to distinguish QRS complexes from the MHD effect but also the gradient artefacts. Different techniques for the suppression of the gradient artefacts are also presented as well as the most challenging problem to-date-the problem of separating the MHD effect from the ECG. The article concludes by summarising the advantages of using ECG signals during MRI, but also presents the current limitations of modern analysis techniques in this domain. The most promising avenues of research are also discussed and suggestions for new methodological analyses for the development of this field are given.

Comparison of three artificial models of the magnetohydrodynamic effect on the electrocardiogram

The electrocardiogram (ECG) is often acquired during magnetic resonance imaging (MRI), but its analysis is restricted by the presence of a strong artefact, called magnetohydrodynamic (MHD) effect. MHD effect is induced by the flow of electrically charged particles in the blood perpendicular to the static magnetic field, which creates a potential of the order of magnitude of the ECG and temporally coincident with the repolarisation period. In this study, a new MHD model is proposed by using MRI-based 4D blood flow measurements made across the aortic arch. The model is extended to several cardiac cycles to allow the simulation of a realistic ECG acquisition during MRI examination and the quality assessment of MHD suppression techniques. A comparison of two existing models, based, respectively, on an analytical solution and on a numerical method-based solution of the fluids dynamics problem, is made with the proposed model and with an estimate of the MHD voltage observed during a real MRI scan. Results indicate a moderate agreement between the proposed model and the estimated MHD model for most leads, with an average correlation factor of 0.47. However, the results demonstrate that the proposed model provides a closer approximation to the observed MHD effects and a better depiction of the complexity of the MHD effect compared with the previously published models, with an improved correlation (+5%), coefficient of determination (+22%) and fraction of energy (+1%) compared with the best previous model. The source code will be made freely available under an open source licence to facilitate collaboration and allow more rapid development of more accurate models of the MHD effect.

ECG-based gating in ultra high field cardiovascular magnetic resonance using an independent component analysis approach

BACKGROUND

In 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.

METHODS

A 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.

RESULTS

ECG 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.

CONCLUSIONS

The 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.