ArticleMagnetic resonance in medicine2019
Accelerating compressed sensing in parallel imaging reconstructions using an efficient circulant preconditioner for cartesian trajectories.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction.IEEE transactions on computational imaging · 2025Article
- A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI.IEEE transactions on computational imaging · 2024Article
- Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.Magnetic resonance in medicine · 2023Article
- Learning a preconditioner to accelerate compressed sensing reconstructions in MRI.Magnetic resonance in medicine · 2022Article
- Image distortion correction for MRI in low field permanent magnet systems with strong BMagma (New York, N.Y.) · 2021Article
- Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning.IEEE transactions on medical imaging · 2020Article
- Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.IEEE signal processing magazine · 2020Article
- Optimization Methods for Magnetic Resonance Image Reconstruction: Key Models and Optimization Algorithms.IEEE signal processing magazine · 2020Article
- [Elimination of Gibbs artifact based on local subpixel shift and interlaced local variation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019Article
- Accelerating compressed sensing in parallel imaging reconstructions using an efficient circulant preconditioner for cartesian trajectories.Magnetic resonance in medicine · 2019Article
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Authors and funding
5 authors.
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Abstract
purposeDesign of a preconditioner for fast and efficient parallel imaging (PI) and compressed sensing (CS) reconstructions for Cartesian trajectories. THEORY: PI and CS reconstructions become time consuming when the problem size or the number of coils is large, due to the large linear system of equations that has to be solved in
methodsIn this article we construct such a preconditioner by approximating the system matrix of the linear system, which comprises the data fidelity and includes total variation and wavelet regularization, by a matrix that is block circulant with circulant blocks. Due to this structure, the preconditioner can be constructed quickly and its inverse can be evaluated fast using only two fast Fourier transformations. We test the performance of the preconditioner for the conjugate gradient method as the linear solver, integrated into the well-established Split Bregman algorithm.
resultsThe designed circulant preconditioner reduces the number of iterations required in the conjugate gradient method by almost a factor of 5. The speed up results in a total acceleration factor of approximately 2.5 for the entire reconstruction algorithm when implemented in MATLAB, while the initialization time of the preconditioner is negligible.
conclusionThe proposed preconditioner reduces the reconstruction time for PI and CS in a Split Bregman implementation without compromising reconstruction stability and can easily handle large systems since it is Fourier-based, allowing for efficient computations.
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