mlsanityData to 14 Oct 2026

On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction

arXiv 2203.16392 ↗First posted 30 Mar 2022eess.IV cs.CVcited by 14

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Abstract (arXiv)

Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory. In this paper, we study the problem of joint learning of the reconstruction model together with acquisition policies. To this end, we extend the End-to-End Variational Network with learnable acquisition policies that can adapt to different data points. We validate our model on a coil-compressed version of the large scale undersampled multi-coil fastMRI dataset using two undersampling factors: 4× and 8×. Our experiments show on-par performance with the learnable non-adaptive and handcrafted equidistant strategies at 4×, and an observed improvement of more than 2% in SSIM at 8× acceleration, suggesting that potentially-adaptive k-space acquisition trajectories can improve reconstructed image quality for larger acceleration factors. However, and perhaps surprisingly, our best performing policies learn to be explicitly non-adaptive.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 30 Mar 2022.

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24 of 52 references.

References · 24

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12 more
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Citing papers per year: 2022: 2; 2023: 5; 2024: 5; 2025: 0; 2026: 2.

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  1. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 38
  2. 2023A comprehensive survey on deep active learning in medical image analysiscited by 35
  3. 2023On Retrospective k-space Subsampling schemes For Deep MRI Reconstructioncited by 12
  4. 2022E-Valuating Classifier Two-Sample Testscited by 10
  5. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2
  6. 2024End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRIcited by 2
  7. 2024Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensingcited by 2
  8. 2022A Path Towards Clinical Adaptation of Accelerated MRIcited by 1
  9. 2023Learning to sample in Cartesian MRIcited by 1
  10. 2024Simulating, Fast and Slow: Learning Policies for Black-Box Optimizationcited by 0
  11. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problemscited by 0
  12. 2024Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRIcited by 0
2 more
  1. 2026Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imagingcited by 0
  2. 2026Active Data Acquisition with Side Information via Discrete Diffusion Priorscited by 0