ArticleIEEE transactions on medical imaging2020
Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Simultaneous TMagnetic resonance in medicine · 2026Article
- Head Motion Correction Based on Pilot Tone Signals in MRI: A Referenceless Method.IEEE transactions on medical imaging · 2026Article
- A New k-Space Model for Non-Cartesian Fourier Imaging.IEEE transactions on computational imaging · 2026Article
- Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning.Nature communications · 2026Article
- Coil Sketching for Fast and Efficient 4D Lung MRI Reconstruction.Magnetic resonance in medicine · 2026Article
- DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.Magnetic resonance in medicine · 2026Article
- Temperature Mapping During MR-Guided Cryoablation Using a FLORET UTE Sequence.Magnetic resonance in medicine · 2026Article
- Compressed sensing acceleration of radial 3-D alternating Look-LockerMagnetic resonance in medicine · 2025Article
- MRI-based quantification of whole-organ renal metabolic rate of oxygen during free-breathing.Magnetic resonance in medicine · 2025Article
- Quantifying spatial and dynamic lung abnormalities with 3D PREFUL FLORET UTE imaging: A feasibility study.Magnetic resonance in medicine · 2025Article
- Article
- Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction.IEEE transactions on computational imaging · 2025Article
- Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.Magnetic resonance in medicine · 2025Article
- A decay-modeled compressed sensing reconstruction approach for non-Cartesian hyperpolarizedMagnetic resonance in medicine · 2024Article
- A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI.IEEE transactions on computational imaging · 2024Article
- Fast Compressed Sensing of 3D Radial TJournal of imaging · 2023Article
- Memory efficient model based deep learning reconstructions for high spatial resolution 3D non-cartesian acquisitions.Physics in medicine and biology · 2023Article
- Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints.Magnetic resonance in medicine · 2023Article
- Learning a preconditioner to accelerate compressed sensing reconstructions in MRI.Magnetic resonance in medicine · 2022Article
- Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
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3 authors.
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Abstract
We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density compensations which sacrifice reconstruction accuracy, or circulant preconditioners which increase per-iteration computation. Our approach overcomes both shortcomings. Concretely, we show that viewing the reconstruction problem in the dual formulation allows us to precondition in k-space using density-compensation-like operations. Using the primal-dual hybrid gradient method, the proposed preconditioning method does not have inner loops and are competitive in accelerating convergence compared to existing algorithms. We derive l2 -optimized preconditioners, and demonstrate through experiments that the proposed method converges in about ten iterations in practice.
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