Low-Latency Reconstruction of Real-Time Cine MRI Using an Unrolled Network

Motivation: Interactive real-time MRI requires low reconstruction latencies. Deep learning-based methods are promising, but unrolled networks like the Variational Network have longer inference times than purely image-based methods. Goal(s): Design and train a Variational Network with high reconstruction quality and inference times suitable for interactive real-time applications. Approach: Modify the Variational Network architecture such that few unrolling steps are sufficient for high reconstruction quality with short inference times. Results: The proposed architectural modifications allowed to halve the number of unrolling steps without compromising image quality, therefore enabling considerably shortened reconstruction times. Impact: Two modifications to an unrolled Variational Network architecture for MRI reconstruction are proposed. These enable reconstructing interactive real-time cardiac cine MRI with high reconstruction quality while maintaining minimal reconstruction latency.