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

作者：Haoyu Sun, Qichen Ding, Sijie Zhong, Zhiyong Zhang · 发表于：Physics in Medicine and Biology · 年份：2025 · DOI：10.1088/1361-6560/ae0c52 · 研究领域：Advanced MRI Techniques and Applications、Cardiac Imaging and Diagnostics、Atomic and Subatomic Physics Research

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 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 scenario...