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- W3202316810 abstract "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 https://github.com/mikgroup/MEL_MRI." @default.
- W3202316810 created "2021-10-11" @default.
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- W3202316810 date "2021-01-01" @default.
- W3202316810 modified "2023-10-01" @default.
- W3202316810 title "Memory-Efficient Learning for High-Dimensional MRI Reconstruction" @default.
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- W3202316810 doi "https://doi.org/10.1007/978-3-030-87231-1_45" @default.
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