Nonlinear Bayesian Filtering for Denoising of Electrocardiograms Acquired in a Magnetic Resonance Environment

Oster, Julien; Pietquin, Olivier; Kraemer, Michel; Felblinger, Jacques

doi:10.1109/tbme.2010.2046324

Cited by 34 publications

(21 citation statements)

References 34 publications

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“…The resulting IC

{\overset{s}{false}}_{k,ECG}

is a linear combination of the original ECG leads. Based on these linear combinations, the gradient artefacts in

{\overset{s}{false}}_{k,ECG}

will still respect the linear time invariant assumption needed for the application of previously published methods for suppressing the gradient artefacts [21,34,35]. …”

Section: Discussionmentioning

confidence: 99%

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

Krug

Rose

Clifford

et al. 2013

Journal of Cardiovascular Magnetic Resonance

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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.

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“…The resulting IC

{\overset{s}{false}}_{k,ECG}

is a linear combination of the original ECG leads. Based on these linear combinations, the gradient artefacts in

{\overset{s}{false}}_{k,ECG}

will still respect the linear time invariant assumption needed for the application of previously published methods for suppressing the gradient artefacts [21,34,35]. …”

Section: Discussionmentioning

confidence: 99%

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

Krug

Rose

Clifford

et al. 2013

Journal of Cardiovascular Magnetic Resonance

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“…This bias is also influenced by the jitter in R wave detection during MRI. ECG detection of the R-wave in MRI environment is known to be difficult (especially at high fields such as in our case) and comes with some jitter, which gets reflected in the computation of the end-systolic time [28]. We tried to minimize this problem by repositioning the detection of the QRS complex in an automatic post-processing step based on pattern matching.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals

et al. 2017

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BackgroundPrediction of End-Systole time is of utmost importance for cardiac MRI to correctly associate acquired k-space lines during reconstruction of cine acquisitions. This prediction is usually based on the patient’s heart rate using Weissler’s formula, which was calibrated by linear regression within a population and cannot account for individual variability.ObjectiveWe propose an automatic method to build a personalized model that better predicts end-systole.MethodsA phase contrast sequence was modified to acquire only central k-space line with 6.6ms temporal resolution, in a slice passing through the aorta during 128 heartbeats in 35 subjects. Segmentation of aorta and detection of end of systolic ejection was automatic. Duration of electromechanical systole duration as function of heart rate was determined for each subject separately.ResultsIn comparison with the global models, the adapted cardiac model predicted significantly better both echocardiographic end-systolic reference (bias = 0ms vs 17ms, p<0.001) and MRI measurements (bias = 6.8ms vs 17ms). Favorable impact was shown on the cine reconstruction of the 5 subjects with the higher cardiac variability (p = 0.02).ConclusionsPersonalization of cardiac model to the subject is feasible in MRI and reduces the error of prediction of systole.

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“…4) a été adaptée pour les ECG arté-factés. Enfin, une approche bayésienne [20] a été proposée pour le débruitage (méthode Bagarre). …”

Section: Traitement Du Signal Ecgunclassified

Capteur ECG intelligent pour la synchronisation des séquences en IRM et le monitorage des patients

Felblinger

Blondé

Jovanovic

et al. 2011

IRBM

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Nonlinear Bayesian Filtering for Denoising of Electrocardiograms Acquired in a Magnetic Resonance Environment

Cited by 34 publications

References 34 publications

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

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

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals

Capteur ECG intelligent pour la synchronisation des séquences en IRM et le monitorage des patients

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