mlsanityData to 14 Oct 2026

End-to-End Sequential Sampling and Reconstruction for MRI

arXiv 2105.06460 ↗First posted 13 May 2021eess.IV cs.CVcited by 13

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Abstract (arXiv)

Accelerated MRI shortens acquisition time by subsampling in the measurement kappa-space. Recovering a high-fidelity anatomical image from subsampled measurements requires close cooperation between two components: (1) a sampler that chooses the subsampling pattern and (2) a reconstructor that recovers images from incomplete measurements. In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy. This co-designed framework is able to adapt during acquisition in order to capture the most informative measurements for a particular target. Experimental results on the fastMRI knee dataset demonstrate that the proposed approach successfully utilizes intermediate information during the sampling process to boost reconstruction performance. In particular, our proposed method can outperform the current state-of-the-art learned kappa-space sampling baseline on over 96% of test samples. We also investigate the individual and collective benefits of the sequential sampling and co-design strategies.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 32 of its 60 references that are in the map. First posted on arXiv 13 May 2021.

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32 of 60 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  6. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  7. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
  8. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  9. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  10. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 232
  11. 2017Image reconstruction by domain transform manifold learningcited by 232
  12. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
20 more
  1. 2017Deep learning for undersampled MRI reconstructioncited by 91
  2. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  3. 2019RARE: Image Reconstruction using Deep Priors Learned without Ground Truthcited by 64
  4. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59
  5. 2020Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imagingcited by 57
  6. 2019Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisitioncited by 56
  7. 2020Active MR k-space Sampling with Reinforcement Learningcited by 46
  8. 2020Experimental design for MRI by greedy policy searchcited by 40
  9. 2019Learning-based Optimization of the Under-sampling Pattern in MRIcited by 33
  10. 2021B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRIcited by 29
  11. 2019Self-Supervised Deep Active Accelerated MRIcited by 24
  12. 2019PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRIcited by 24
  13. 2020Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRIcited by 22
  14. 2018Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imagingcited by 18
  15. 2019Data-Driven Design for Fourier Ptychographic Microscopycited by 18
  16. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  17. 2019Joint learning of cartesian undersampling and reconstruction for accelerated MRIcited by 14
  18. 2015Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstructioncited by 10
  19. 2020Learning a Probabilistic Strategy for Computational Imaging Sensor Selectioncited by 8
  20. 2013Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRIcited by 5

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Citing papers per year: 2021: 1; 2022: 7; 2023: 1; 2024: 4.

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  1. 2022Symbolic Learning to Optimize: Towards Interpretability and Scalabilitycited by 16
  2. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  3. 2024Probabilistic Bayesian optimal experimental design using conditional normalizing flowscited by 10
  4. 2022JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRIcited by 3
  5. 2021Single-pass Object-adaptive Data Undersampling and Reconstruction for MRIcited 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. 2022Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Frameworkcited by 1
  9. 2022A Path Towards Clinical Adaptation of Accelerated MRIcited by 1
  10. 2023Learning to sample in Cartesian MRIcited by 1
  11. 2022A Note on Machine Learning Approach for Computational Imagingcited by 0
  12. 2022L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement Learningcited by 0
1 more
  1. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problemscited by 0