Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging

Hoppe E, Wetzl J, Yoon SS, Bacher M, Roser P, Stimpel B, Preuhs A, Maier A (2021)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2021

Journal

DOI: 10.1109/tmi.2021.3073091

Abstract

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, timecontinous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of >98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.

Authors with CRIS profile

Involved external institutions

Siemens AG, Healthcare Sector Germany (DE) Center for Biomedical Imaging / Centre d'Imagerie BioMedicale (CIBM) Switzerland (CH)

How to cite

APA:

Hoppe, E., Wetzl, J., Yoon, S.S., Bacher, M., Roser, P., Stimpel, B.,... Maier, A. (2021). Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging. https://dx.doi.org/10.1109/tmi.2021.3073091

MLA:

Hoppe, Elisabeth, et al. "Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging." IEEE Transactions on Medical Imaging (2021).

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