Computationally Efficient IMplicit Training Strategy for UNrolled NEtworks (IMUNNE) for Reconstruction of Accelerated Cardiac Dynamic MRI

Motivation: Unrolled networks (UN) achieve state-of-the-art performance in undersampled dynamic MRI reconstruction but suffer from long training times and extensive GPU memory cost. Goal(s): To apply an implicit training strategy for UNs (IMUNNE) in combination with transfer learning to develop an efficient and versatile reconstruction technique for accelerated dynamic cardiac MRI. Approach: We compare IMUNNE with a complex denoiser U-Net and an end-to-end UN on three different highly undersampled dynamic cardiac MRI datasets. Results: For all datasets, we observed that: (1) both unrolled architectures outperform CU-Net with respect to image quality; (2) compared to end-to-end UN, IMUNNE significantly reduced both training and inference times. Impact: This work has the potential to facilitate a more widespread adoption of highly-accelerated, cardiac MRI by reducing training time, inference time and memory cost of state-of-the-art unrolled reconstruction methods, thereby lowering the clinical hardware requirements and the requisite energy consumption.