  Purpose: We implemented the Machine Learning (ML) aided k-t SENSE
reconstruction to enable high resolution quantitative real-time phase contrast
MR (PCMR). Methods: A residual U-net and our U-net M were used to generate the
high resolution x-f space estimate for k-t SENSE regularisation prior. The
networks were judged on their ability to generalise to real undersampled data.
The in-vivo validation was done on 20 real-time 18x prospectively undersmapled
GASperturbed PCMR data. The ML aided k-t SENSE reconstruction results were
compared against the free-breathing Cartesian retrospectively gated sequence
and the compressed sensing (CS) reconstruction of the same data. Results: In
general, the ML aided k-t SENSE generated flow curves that were visually
sharper than those produced using CS. In two exceptional cases, U-net M
predictions exhibited blurring which propagated to the extracted velocity
curves. However, there were no statistical differences in the measured peak
velocities and stroke volumes between the tested methods. The ML aided k-t
SENSE was estimated to be ~3.6x faster in processing than CS. Conclusion: The
ML aided k-t SENSE reconstruction enables artefact suppression on a par with CS
with no significant differences in quantitative measures. The timing results
suggest the on-line implementation could deliver a substantial increase in
clinical throughput.
