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Abstract #0328

Deep Learning-Driven ECG Synthesis from Beat Pilot Tone Signals for Physiological Monitoring and Image Reconstruction

Haoyu Sun1, Sijie Zhong1, Qichen Ding1, Philip Kenneth Lee 1, and Zhiyong Zhang1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Motion Correction, Motion Correction

Motivation: The electrocardiogram (ECG) is vital in MRI for cardiac gating and physiological monitoring. However, conventional ECG requires adhesive electrodes, which may compromise patient comfort and imaging experience.

Goal(s): Achieve contactless ECG monitoring during MR scans.

Approach: We captured cardiac mechanical motion using the beat pilot tone method and developed a deep learning network to achieve end-to-end mapping to ECG.

Results: In our best-case results, contactless ECG measurements achieve trigger timing accuracy with a 19.6±8.6 ms error and 0.0059 Root-Mean-Square-Error compared to ground truth ECG. Physiological signals from our method and Siemens Physio Suite were each used for XD-GRASP reconstruction, producing comparable image quality.

Impact: This work explores the potential for contactless ECG monitoring during MRI, improving patient comfort and workflow. Compared to BPT-derived cardiac signals, the synthesized ECG better suits clinicians' interpretation habits, potentially enhancing physiological monitoring. Additionally, it offers sharper signals for triggering.

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