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Experimental design for MRI by greedy policy search

arXiv 2010.16262 ↗First posted 30 Oct 2020cs.CV cs.LG cs.NEcited by 40

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Abstract (arXiv)

In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain. Currently, the construction of these subsampling strategies - known as experimental design - relies primarily on heuristics. We propose to learn experimental design strategies for accelerated MRI with policy gradient methods. Unexpectedly, our experiments show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We offer a partial explanation for this phenomenon rooted in greater variance in the non-greedy objective's gradient estimates, and experimentally verify that this variance hampers non-greedy models in adapting their policies to individual MR images. We empirically show that this adaptivity is key to improving subsampling designs.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 6; 2024: 11; 2025: 5; 2026: 5.

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  1. 2021Active 3D Shape Reconstruction from Vision and Touchcited by 34
  2. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  3. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  4. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  5. 2023On Retrospective k-space Subsampling schemes For Deep MRI Reconstructioncited by 12
  6. 2024Adaptive Compressed Sensing with Diffusion-Based Posterior Samplingcited by 12
  7. 2022Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networkscited by 11
  8. 2022E-Valuating Classifier Two-Sample Testscited by 10
  9. 2021Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Taskscited by 7
  10. 2024Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Predictioncited by 7
  11. 2024Active Diffusion Subsamplingcited by 7
  12. 2024Sequential Posterior Sampling with Diffusion Modelscited by 6
28 more
  1. 2021Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolutioncited by 5
  2. 2021Towards Robust Active Feature Acquisitioncited by 4
  3. 2022Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learningcited 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. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  7. 2024Indirectly Parameterized Concrete Autoencoderscited by 3
  8. 2024End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRIcited by 2
  9. 2024The MRI Scanner as a Diagnostic: Image-less Active Samplingcited by 2
  10. 2024Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensingcited by 2
  11. 2022Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Frameworkcited by 1
  12. 2022A Path Towards Clinical Adaptation of Accelerated MRIcited by 1
  13. 2023On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosiscited by 1
  14. 2023Learning to sample in Cartesian MRIcited by 1
  15. 2024Variational Learning ISTAcited by 1
  16. 2025Active Sampling for MRI-based Sequential Decision Makingcited by 1
  17. 2025Online Feedback Efficient Active Target Discovery in Partially Observable Environmentscited by 1
  18. 2025Replacing thinking with tool usage enables reasoning in small language modelscited by 1
  19. 2022L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement Learningcited by 0
  20. 2023Reinforcement Learning for Sampling on Temporal Medical Imaging Sequencescited by 0
  21. 2024Simulating, Fast and Slow: Learning Policies for Black-Box Optimizationcited by 0
  22. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problemscited by 0
  23. 2025Active MRI Acquisition with Diffusion Guided Bayesian Experimental Designcited by 0
  24. 2026MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRIcited by 0
  25. 2026Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imagingcited by 0
  26. 2026Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsamplingcited by 0
  27. 2026Conformalized Rate-Adaptive Sensingcited by 0
  28. 2026OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRIcited by 0