Jaubert, Olivier;
Montalt-Tordera, Javier;
Brown, James;
Knight, Daniel;
Arridge, Simon;
Steeden, Jennifer;
Muthurangu, Vivek;
(2022)
FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation.
Magnetic Resonance in Medicine
10.1002/mrm.29374.
(In press).
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Abstract
Purpose: Real-time monitoring of cardiac output (CO) requires low-latency reconstruction and segmentation of real-time phase-contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for “FReSCO” (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring). Methods: Deep artifact suppression and segmentation U-Nets were independently trained. Breath-hold spiral phase-contrast MR data (N = 516) were synthetically undersampled using a variable-density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U-net. A subset of the data (N = 96) was segmented and used to train the segmentation U-net. Real-time spiral phase-contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed-sensing CO. Results: The FReSCO framework was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = −0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458). Conclusions: The FReSCO framework was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.
| Type: | Article |
|---|---|
| Title: | FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/mrm.29374 |
| Publisher version: | https://doi.org/10.1002/mrm.29374 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Radiology, Nuclear Medicine & Medical Imaging, Cardiac MRI, deep learning, Cardiac output, monitoring, real-time, Flow imaging, PHASE-CONTRAST MR, REAL-TIME FLOW, RESOLUTION, AORTA |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154138 |
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