ICA-based gating method
Although it was shown previously that ICA is not suitable for recovering the diagnostic information contained in the MHD-contaminated ECG signal[
28], ICA was successfully applied to 12-lead ECG signals for the real-time estimation of an IC
which was dominated by the R-peak. This IC was then used for R-peak detection. The influence of different ECG leads on the results achieved by ICA was investigated by applying ICA to different lead combinations. The best R-peak detection results were achieved with lead configuration LC1 where eight ECG leads (I,II,V1-V6) were used for the estimation of
. For the combinations of only three ECG leads used with ICA, it was shown that the best average R-peak detection results were achieved by a combination of the two limb leads I and II and of the precordial lead V4. These experiments emphasized the importance of the precordial leads – especially of lead V4 – for the proposed method. Not using the precordial leads as in configuration LC2h caused a substantial decrease in R-peak detection performance (Table
3).
For 7 T CMR application based on a prospective gating scheme, the mean trigger propagation delay
μpd should be less than 20 ms because the systole’s mechanical contraction begins 30 ms-70 ms after the R-peak[
29]. A large jitter
σpd could lead to blurring of the CMR image, to ghost artefacts in the phase encoding direction or to a false estimation of the blood flow rate in phase contrast MRI. For the estimation of blood flow rates, the jitter should be less than 15 ms[
30]. Using the proposed ICA-based method with lead configuration LC1, a mean detection delay
μpd of 5.8 ms and a jitter
σpd of 5.0 ms were achieved in the test dataset using eight ECG leads in lead configuration LC1. Hence, the usage of this method would enable the acquisition of proper CMR images.
The demixing matrix W was only estimated once for each dataset using a 30 s segment of the recorded ECG signal and was reduced to a demixing vector w after was identified. This allowed a demixing of the different signal components and the estimation of in real-time. W was stable over a long period of time (1 year) which was shown using demixing matrices from previously recorded 7 T datasets of the same volunteer – even though the ECG electrode positions slightly varied between both measurements. Besides, the demixing matrix W was obtained from and applied to datasets which were acquired during normal breathing. Large baseline shifts caused by the physical movement of the ECG electrodes during breathing can affect the ECG signals. These variations can complicate the R-peak detection. The results obtained in this study show that the proposed method for the estimation of can be applied over a long period of time and is robust against variations caused by normal breathing.
The demixing matrices
W varied between the different datasets D
1-D
9. It was however noticed that the precordial leads were contributing more for the generation of the IC
. Nevertheless, the combination of the precordial leads varied over the subject population, and it was also shown in this study that the limb leads add information and allow for a better separation of the MHD and the ECG. Two reasons can be given for the variation of the demixing matrices. Firstly, the anatomy and physiology of the different volunteers leads to high variations of the MHD signals as it is shown in Figure
4 and was previously discussed in[
31]. Hence, the ECG leads were not affected in the same way in the datasets D
1-D
9 and different combinations of the measured ECG leads
x
k
were required to obtain an IC
which was suitable for R-peak detection. Secondly, this phenomenon can be explained by the positions of the ECG electrodes with respect to the position and orientation of the heart’s electrical axis. These anatomical parameters vary between the different volunteers or datasets. As a result, the ECG traces – either recorded outside or inside the MR scanner – vary between the different volunteers. This requires one initial computation of the demixing matrix
W for each new subject. In this work it was shown that ICA provides an elegant way of estimating
W for different subjects without further assumptions.
A similar ICA-based method was utilized previously for the suppression of gradient artefacts from 3-lead ECG signals acquired during MRI[
21]. These gradient induced artefacts exhibit temporal variations due to different MRI sequences, slice locations and orientations. Hence, a continuous update of the demixing matrix was required during runtime. As described in this work, an update of the demixing matrix for the suppression of the MHD effect using the 12-lead ECG signals was not necessary.
Given the results discussed above, the presented ICA-based method is suitable to be used in 7 T CMR imaging for the real-time estimation of an IC which can then be used for R-peak detection and gating.
Comparison to other gating methods
From the other R-peak detection techniques M1-M5, method M2 using VCG lead x gave the best results. For method M1 which utilized the original ECG signal, the best R-peak detection results were achieved using the precordial ECG lead V4. Together with the results obtained by the ICA-based method, this again emphasizes the importance of the precordial leads for R-peak detection in 7 T CMR.
The results obtained for the state-of-the-art VCG-based gating method (M3) proposed in[
5,
13] showed low
Se and +
P values which are insufficient for gating at 7 T CMR. Based on the functional principle of the VCG method, a modified reference vector was suggested which increased +
P within the test dataset by approximately 21%. Although this modification improved the VCG-based technique, results are still not sufficient for a proper gating in CMR. Furthermore, the required manual R-peak annotation makes this modification impractical for a clinical application.
Future work
The demixing matrix W was only estimated once for each dataset. Where this approach was successfully applied to nine datasets D1-D9 of healthy volunteers, problems could arise for patients with cardiac pathologies. For such cases, a recalculation of W could be required during runtime. This needs to be evaluated with additional datasets of volunteers or patients suffering from myocardial pathologies.
Due to the lack of MR-conditional 12-lead ECG hardware devices, contaminations caused by the switched gradient magnetic fields of the MR scanner have not been investigated. In this case, however, the demixing matrix
W would be computed before playing the gradients. The resulting IC
is a linear combination of the original ECG leads. Based on these linear combinations, the gradient artefacts in
will still respect the linear time invariant assumption needed for the application of previously published methods for suppressing the gradient artefacts[
21,
34,
35].