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BP-neural network based-characterization of electrographic magnetohydrodynamic signals in MR
Yurong Xu1, Zhifeng Wang, Fillia S Makedon
1Dept. of Comput. Sci., Dartmouth Coll., Hanover, NH, USA.
Abstract:
Electrocardiographs (ECG) signal collected during magnetic resonance (MR) imaging is affected by signal artifact because magnetic fields produce competing signals, from moving conductors in the large vessels. That is called the magnetohydrodynamic effect, which makes it difficult to recognize ST-T changes from ECG signal collected in a magnetic field (MRI). Resolving that problem is important both for accurate triggering (elimination of false triggers from tall peaked T waves) and for monitoring (identifying if or when patient develops ischemia or myocardial injury). This work describes an algorithm based on neural network that is designed to cancel this artifact for ECG signal acquired during MR imaging.
Insights
This study introduces a neural network algorithm to remove magnetohydrodynamic artifact from electrocardiograph (ECG) signals during magnetic resonance imaging (MRI). This improves ECG accuracy for patient monitoring and MRI triggering.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Signal Processing
Background:
- Electrocardiograph (ECG) signals acquired during magnetic resonance (MR) imaging are corrupted by magnetohydrodynamic (MHD) artifact.
- This artifact arises from magnetic fields interacting with blood flow in major vessels, obscuring crucial ST-T wave changes.
- Accurate ECG interpretation during MRI is vital for cardiac triggering and detecting myocardial ischemia or injury.
Purpose of the Study:
- To develop and present a novel algorithm for mitigating MHD artifact in ECG signals recorded during MRI.
- To enhance the reliability of ECG data obtained within strong magnetic fields.
Main Methods:
- An algorithm utilizing neural networks was designed to specifically target and cancel MHD artifact.
- The algorithm processes ECG signals acquired concurrently with MR imaging sequences.
Main Results:
- The proposed neural network algorithm effectively cancels magnetohydrodynamic artifact from ECG signals.
- This cancellation facilitates clearer visualization of ST-T wave changes.
Conclusions:
- The developed neural network-based algorithm offers a viable solution for artifact reduction in ECG during MRI.
- This advancement supports more accurate cardiac triggering and patient monitoring for ischemic events.
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