  Deep learning (DL) based unrolled reconstructions have shown state-of-the-art
performance for under-sampled magnetic resonance imaging (MRI). Similar to
compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time,
3D+time) to further improve performance. However, network size and depth are
currently limited by the GPU memory required for backpropagation. Here we use a
memory-efficient learning (MEL) framework which favorably trades off storage
with a manageable increase in computation during training. Using MEL with
multi-dimensional data, we demonstrate improved image reconstruction
performance for in-vivo 3D MRI and 2D+time cardiac cine MRI. MEL uses far less
GPU memory while marginally increasing the training time, which enables new
applications of DL to high-dimensional MRI.
