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Active MR k-space Sampling with Reinforcement Learning

arXiv 2007.10469 ↗First posted 20 Jul 2020eess.IV cs.CVcited by 45

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Abstract (arXiv)

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2020.

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Citing papers per year: 2021: 8; 2022: 10; 2023: 6; 2024: 9; 2025: 4; 2026: 8.

45 papers.

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  1. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  2. 2021Active 3D Shape Reconstruction from Vision and Touchcited by 34
  3. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  4. 2022DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstructioncited by 30
  5. 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
  6. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  7. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  8. 2024Adaptive Compressed Sensing with Diffusion-Based Posterior Samplingcited by 12
  9. 2022E-Valuating Classifier Two-Sample Testscited by 10
  10. 2021Deep reinforcement learning-designed radiofrequency waveform in MRIcited by 8
  11. 2022RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learningcited by 8
  12. 2021Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Taskscited by 7
33 more
  1. 2024Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Predictioncited by 7
  2. 2022Reproducibility of the Methods in Medical Imaging with Deep Learningcited by 5
  3. 2021Towards Robust Active Feature Acquisitioncited by 4
  4. 2025Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomographycited by 4
  5. 2022JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRIcited by 3
  6. 2022Active CT Reconstruction with a Learned Sampling Policycited by 3
  7. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  8. 2023Learning Task-Specific Strategies for Accelerated MRIcited by 3
  9. 2021Single-pass Object-adaptive Data Undersampling and Reconstruction for MRIcited by 2
  10. 2024End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRIcited by 2
  11. 2024The MRI Scanner as a Diagnostic: Image-less Active Samplingcited by 2
  12. 2024Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensingcited by 2
  13. 2022Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Frameworkcited by 1
  14. 2023Learning to sample in Cartesian MRIcited by 1
  15. 2024Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Modelscited by 1
  16. 2024Artificial Intelligence for Neuro MRI Acquisition: A Reviewcited by 1
  17. 2025Active Sampling for MRI-based Sequential Decision Makingcited by 1
  18. 2025CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRIcited by 1
  19. 2025On The Role of K-Space Acquisition in MRI Reconstruction Domain-Generalizationcited by 1
  20. 2022A Note on Machine Learning Approach for Computational Imagingcited by 0
  21. 2022L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement Learningcited by 0
  22. 2023Control of a simulated MRI scanner with deep reinforcement learningcited by 0
  23. 2023Reinforcement Learning for Sampling on Temporal Medical Imaging Sequencescited by 0
  24. 2024Simulating, Fast and Slow: Learning Policies for Black-Box Optimizationcited by 0
  25. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problemscited by 0
  26. 2026MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRIcited by 0
  27. 2026Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imagingcited by 0
  28. 2026Sequence Search: Automated Sequence Design using Neural Architecture Searchcited by 0
  29. 2026Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigmcited by 0
  30. 2026Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsamplingcited by 0
  31. 2026Conformalized Rate-Adaptive Sensingcited by 0
  32. 2026Active Data Acquisition with Side Information via Discrete Diffusion Priorscited by 0
  33. 2026OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRIcited by 0