Real-time deep learning non-Cartesian image reconstruction using a causal variational network

Real-time MRI captures movements and dynamic processes in human body without reliance on any repetition or synchronization. Many applications require low-latency (typically <200ms) for guidance of interventions or closed-loop feedback. Standard constrained optimization methods are too slow to be implemented “online”. We demonstrate a non-Cartesian deep learning image reconstruction method based on the end-to-end variational network. Training data are created using traditional high latency compressed sensing reconstruction as the reference, with the goal of achieving similar results with low latency. We demonstrate reconstruction latencies of 95ms per frame, with nRMSE of 0.045 and SSIM of 0.95.