mlsanityData to 7 Oct 2026

Memory-efficient Learning for High-Dimensional MRI Reconstruction

arXiv 2103.04003 ↗First posted 6 Mar 2021eess.IV cs.CVcited by 5

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Abstract (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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 6 Mar 2021.

Builds on

13 of 25 references.

References · 13

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1 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2021High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Losscited by 6
  2. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  3. 2022GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstructioncited by 3
  4. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  5. 2026Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRIcited by 0