ArticleIEEE transactions on computational imaging2020
Deep-learning-based Optimization of the Under-sampling Pattern in MRI.
Article in IEEE transactions on computational imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.Magnetic resonance in medicine · 2026Article
- Well-designed k-space coverage is important for good MRI denoising.Magma (New York, N.Y.) · 2026Article
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- Enhancing cardiotocography classification via ensemble learning and threshold optimization.Scientific reports · 2025Article
- Improved Spiral Projection MR Fingerprinting via Memory-Efficient Synergic Optimization of 3D Spiral Trajectory, Image Reconstruction and Parameter Estimation (SOTIP).IEEE transactions on medical imaging · 2025Article
- Systems Engineering Approach Towards Sensitive Cellular Fluorine-19 MRI.NMR in biomedicine · 2025Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Promoting fast MR imaging pipeline by full-stack AI.iScience · 2024Article
- Complexities of deep learning-based undersampled MR image reconstruction.European radiology experimental · 2023Review
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Bayesian reconstruction of magnetic resonance images using Gaussian processes.Scientific reports · 2023Article
- Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.Magnetic resonance in medicine · 2023Article
- LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping.NeuroImage · 2023Article
- K2S Challenge: From Undersampled K-Space to Automatic Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Jointly Learning Non-CartesianBioengineering (Basel, Switzerland) · 2023Article
- Efficient Approximation of Jacobian Matrices Involving a Non-Uniform Fast Fourier Transform (NUFFT).IEEE transactions on computational imaging · 2023Article
- AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.Frontiers in neuroimaging · 2023Article
- De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates.Bioengineering (Basel, Switzerland) · 2022Article
- Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.IEEE transactions on medical imaging · 2022Article
- A Path Towards Clinical Adaptation of Accelerated MRI.Proceedings of machine learning research · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this paper, we tackle both problems simultaneously for the specific case of 2D Cartesian sampling, using a novel end-to-end learning framework that we call LOUPE (Learning-based Optimization of the Under-sampling PattErn). Our method trains a neural network model on a set of full-resolution MRI scans, which are retrospectively under-sampled on a 2D Cartesian grid and forwarded to an anti-aliasing (a.k.a. reconstruction) model that computes a reconstruction, which is in turn compared with the input. This formulation enables a data-driven optimized under-sampling pattern at a given sparsity level. In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large-scale, publicly available knee MRI dataset, where LOUPE offered superior reconstruction quality across different conditions. Even with an aggressive 8-fold acceleration rate, LOUPE's reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs knee MRI scans. Our code is made freely available at https://github.com/cagladbahadir/LOUPE/.
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