Stella X. Yu : Papers / Google Scholar

Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang and Michael Kellman and Christopher M. Sandino and Kevin Zhang and Shreyas S. Vasanawala and Jonathan I. Tamir and Stella X. Yu and Michael Lustig
International Conference on Medical Image Computing and Computer Assisted Intervention, Online, 27 September - 1 October 2021
Paper | Poster | arXiv

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. Our code is available at \url{}.

magnetic resonance imaging, unrolled reconstruction, memory-efficient learning