  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 and recovery periods. CO obtained via FReSCO was compared
to a reference rest CO and rest and exercise Compressed Sensing (CS) CO.
  Results: FReSCO was demonstrated prospectively at the scanner. Beat-to-beat
heartrate, stroke volume and CO could be visualized with a mean latency of
622ms. No significant differences were noted when compared to reference at rest
(Bias = -0.21+-0.50 L/min, p=0.246) or CS at peak exercise (Bias=0.12+-0.48
L/min, p=0.458).
  Conclusion: FReSCO 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.
