FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
Olivier Jaubert, Javier Montalt-Tordera, James Brown, Daniel Knight,, Simon Arridge, Jennifer Steeden, Vivek Muthurangu

TL;DR
FReSCO is a deep learning framework enabling real-time cardiac output monitoring from phase contrast MRI with low latency, high accuracy, and potential for stress testing applications.
Contribution
The paper introduces a novel deep learning approach for low latency reconstruction and segmentation of PCMR data, enabling real-time cardiac output monitoring during exercise.
Findings
FReSCO achieved a mean latency of 622ms for real-time CO visualization.
No significant difference in CO measurements compared to reference and CS methods.
Successfully demonstrated real-time CO monitoring during rest and exercise.
Abstract
Purpose: Real-time monitoring of cardiac output (CO) requires low latency reconstruction and segmentation of real-time phase contrast MR (PCMR), which has previously been difficult to perform. Here we propose a deep learning framework for 'Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring' (FReSCO). Methods: Deep artifact suppression and segmentation U-Nets were independently trained. Breath hold spiral PCMR data (n=516) was 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 PCMR 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…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Atomic and Subatomic Physics Research · Cardiac Imaging and Diagnostics
