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Deep Learning R-Wave Detection for Electrocardiographic Gating in Cardiac MRI.

Amin Mahmoodi^1,2, Melina Hosseiny³, Vladimir Ermakov³

¹Shu Chien-Gene Lay Department of Bioengineering, University of California-San Diego, La Jolla, Calif.

Radiology. Cardiothoracic Imaging

|November 6, 2025

Summary

This summary is machine-generated.

Convolutional Neural Networks (CNNs) effectively detect R-waves in electrocardiographic (ECG) gating for cardiac MRI, reducing errors, especially at 3.0 T. This deep learning approach enhances ECG gating and cardiac MRI quality.

Keywords:

Cardiac MRI Reconstruction Convolutional Neural Network Deep Learning R-Wave Detection ECG Gating

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Cardiovascular Imaging

Background:

Electrocardiographic (ECG) gating is crucial for high-quality cardiac MRI.
MRI-induced artifacts, particularly at 3.0 T, can lead to ECG-gating errors.
Traditional signal processing algorithms may struggle with artifact-laden ECG signals.

Purpose of the Study:

To quantify ECG-gating error frequency in cine cardiac MRI at 1.5 T and 3.0 T.
To evaluate the efficacy of Convolutional Neural Networks (CNNs) in reducing these errors.

Main Methods:

Retrospective analysis of ECG tracings from 120 cardiac MRI patients (1.5 T and 3.0 T) and an external dataset.
Manual R-wave annotation for determining arrhythmia and ECG-gating error frequency.
Development and testing of a CNN for R-wave detection, compared against VCG gating and the Hamilton algorithm.

Main Results:

ECG-gating errors occurred in 8.1% of patients at 1.5 T and 15.2% at 3.0 T.
CNNs achieved higher F1 scores than VCG (1.5 T) and the Hamilton algorithm (3.0 T).
CNNs demonstrated a significantly lower false-positive rate at 3.0 T compared to the Hamilton algorithm.

Conclusions:

CNNs provide a robust solution for R-wave detection in cardiac MRI, mitigating MRI-induced artifacts.
The use of CNNs can significantly enhance ECG gating accuracy and improve overall cardiac MR image quality.
Deep learning methods show promise for improving retrospective cardiac MRI reconstruction and quality.