ArticleIEEE transactions on computational imaging2024
A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI.
Article in IEEE transactions on computational imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A New k-Space Model for Non-Cartesian Fourier Imaging.IEEE transactions on computational imaging · 2026Article
- A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers.IEEE transactions on computational imaging · 2026Article
- Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.IEEE transactions on computational imaging · 2025Article
- Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction.IEEE transactions on computational imaging · 2025Article
- Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction.IEEE transactions on computational imaging · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Model-based methods are widely used for reconstruction in compressed sensing (CS) magnetic resonance imaging (MRI), using regularizers to describe the images of interest. The reconstruction process is equivalent to solving a composite optimization problem. Accelerated proximal methods (APMs) are very popular approaches for such problems. This paper proposes a complex quasi-Newton proximal method (CQNPM) for the wavelet and total variation based CS MRI reconstruction. Compared with APMs, CQNPM requires fewer iterations to converge but needs to compute a more challenging proximal mapping called weighted proximal mapping (WPM). To make CQNPM more practical, we propose efficient methods to solve the related WPM. Numerical experiments on reconstructing non-Cartesian MRI data demonstrate the effectiveness and efficiency of CQNPM.
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