What type of study is this?

This is a Quantitative Study study (also classified as: Experimental Study).

September 28, 2025

Deep learning-driven contactless ECG in MRI via beat pilot tone for motion-resolved image reconstruction and heart rate monitoring

Key Points

The contactless ECG method achieved a median Pearson correlation of 89% with ground truth ECG data, improving synchronization.
Cardiac trigger accuracy reached 94%, indicating significant reliability in diverse clinical conditions encountered during MRI scans.
This approach addresses the challenges of traditional ECG methods, enhancing patient safety and reducing motion artifacts during imaging.
The integration of deep learning with Beat Pilot Tone allows scalable application across different MRI field strengths, aiding clinical workflows.

Abstract

Abstract Objective. Electrocardiogram (ECG) is crucial for synchronizing cardiovascular magnetic resonance imaging (CMRI) acquisition with the cardiac cycle and for continuous heart rate monitoring during prolonged scans. However, conventional electrode-based ECG systems in clinical MRI environments suffer from tedious setup, magnetohydrodynamic (MHD) waveform distortion, skin burn risks, and patient discomfort. This study proposes a contactless ECG measurement method in MRI to address these challenges. Approach. We integrated Beat Pilot Tone (BPT)—a contactless, high motion sensitivity, and easily integrable RF motion sensing modality—into CMRI to capture cardiac motion without direct patient contact. A deep neural network was trained to map the BPT-derived cardiac mechanical motion signals to corresponding ECG waveforms. The reconstructed ECG was evaluated against simultaneously acquired ground truth ECG through multiple metrics: Pearson correlation coefficient, relative root mean square error (RRMSE), cardiac trigger timing accuracy, and heart rate estimation error. Additionally, we performed MRI retrospective binning reconstruction using reconstructed ECG reference and evaluated image quality under both standard clinical conditions and challenging scenarios involving arrhythmias and subject motion. To examine scalability of our approach across field strength, the model pretrained on 1.5T data was applied to 3T BPT cardiac acquisitions. Main Results. In optimal acquisition scenarios, the reconstructed ECG achieved a median Pearson correlation of 89% relative to the ground truth, while cardiac triggering accuracy reached 94%, and heart rate estimation error remained below 1 bpm. The quality of the reconstructed images was comparable to that of ground truth synchronization. The method exhibited a degree of adaptability to irregular heart rate patterns and subject motion, and scaled effectively across MRI systems operating at different field strengths. Significance. The proposed contactless ECG measurement method has the potential to streamline CMRI workflows, improve patient safety and comfort, mitigate MHD distortion challenges and find a robust clinical application.

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