OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI
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Abstract (arXiv)
This paper introduces a new estimation-theoretic framework for experiment design in the context of MR image reconstruction under sparsity constraints. The new framework is called OEDIPUS (Oracle-based Experiment Design for Imaging Parsimoniously Under Sparsity constraints), and is based on combining the constrained Cram'er-Rao bound with classical experiment design techniques. Compared to popular random sampling approaches, OEDIPUS is fully deterministic and automatically tailors the sampling pattern to the specific imaging context of interest (i.e., accounting for coil geometry, anatomy, image contrast, etc.). OEDIPUS-based experiment designs are evaluated using retrospectively subsampled in vivo MRI data in several different contexts. Results demonstrate that OEDIPUS-based experiment designs have some desirable characteristics relative to conventional MRI sampling approaches.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 2; 2025: 0; 2026: 2.
11 papers.
- 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
- 2022Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networkscited by 11
- 2021Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applicationscited by 6
- 2022LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mappingcited by 6
- 2022PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRIcited by 5
- 2021ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Datacited by 4
- 2026Bayesian Uncertainty-Aware MRI Reconstructioncited by 3
- 2023On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosiscited by 1
- 2024Artificial Intelligence for Neuro MRI Acquisition: A Reviewcited by 1
- 2024LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imagingcited by 0
- 2026Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Modelscited by 0
- 2026Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models15 Jun 2026
- 2026Bayesian Uncertainty-Aware MRI Reconstruction13 Mar 2026
- 2024LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging8 Aug 2024
- 2024Artificial Intelligence for Neuro MRI Acquisition: A Review10 Jun 2024
- 2023On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis23 Jun 2023
- 2022LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping1 Nov 2022
- 2022PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI24 Apr 2022
- 2022Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks5 Apr 2022
- 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction30 Mar 2022
- 2021Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications27 Oct 2021
- 2021ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data16 Mar 2021