Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging.

Details

Serval ID
serval:BIB_6C59173C6013
Type
Article: article from journal or magazin.
Collection
Publications
Title
Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging.
Journal
IEEE transactions on medical imaging
Author(s)
Hoppe E., Wetzl J., Yoon S.S., Bacher M., Roser P., Stimpel B., Preuhs A., Maier A.
ISSN
1558-254X (Electronic)
ISSN-L
0278-0062
Publication state
Published
Issued date
08/2021
Peer-reviewed
Oui
Volume
40
Number
8
Pages
2105-2117
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
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, time-continous 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.
Keywords
Deep Learning, Electrocardiography, Heart/diagnostic imaging, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Motion
Pubmed
Web of science
Create date
13/08/2021 15:32
Last modification date
15/12/2021 13:04
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