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

Abstract

Purpose To measure the frequency of electrocardiographic (ECG) gating errors and to assess whether convolutional neural networks (CNNs) can reduce such errors. Materials and Methods ECG tracings from 120 patients who underwent cine cardiac MRI at 1.5 T and 3.0 T, along with an external dataset of ECG tracings from 47 patients, were retrospectively collected (August 2022-April 2023). The frequency of arrhythmias and ECG-gating errors was determined by manual annotation of R waves. A CNN was developed for R-wave detection using data from 79 MRI and six external patients; the remaining data were used for testing. CNN performance was compared using signal processing algorithms, including vectorcardiographic (VCG) gating and the Hamilton algorithm. Results The mean age of the 120 patients undergoing cardiac MRI was 53.7 years ± 19.8 (SD), with 72 male patients. ECG-gating errors were observed in 8.1% of patients at 1.5 T and 15.2% at 3.0 T. At 1.5 T, CNN achieved a higher F1 score (99.1%) than did VCG (98.1%; P = .048). At 3.0 T, CNN achieved a higher F1 score (99.1%) than did the Hamilton algorithm (92.4%; P = .049) and was not statistically different than VCG (95.1%; P = .68). The CNN demonstrated a lower false-positive rate (0.1%) than did the Hamilton algorithm (7.4%; P = .02) at 3.0 T and was not statistically different than VCG (4.5%; P = .28). The feasibility of retrospective image reconstruction to improve cardiac MRI quality was also demonstrated. Conclusion CNNs offer a robust method for ECG R-wave detection, particularly in the presence of MRI-induced artifacts at 3.0 T. These findings support the potential of CNNs to enhance ECG gating and improve cardiac MR image quality. Keywords: Convolutional Neural Network, ECG Gating, Deep Learning R-Wave Detection, Cardiac MRI Reconstruction Supplemental material is available for this article. © RSNA, 2025.