ArticleIEEE transactions on computational imaging2020
Efficient Regularized Field Map Estimation in 3D 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 5 papers.
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Who cites it
5 citing papers in PubMed.
- BOLD fMRI at 9.4T with 3D stack-of-spirals readouts.Magma (New York, N.Y.) · 2026Article
- Data Stitching for Dynamic Field Monitoring With NMR Probes.Magnetic resonance in medicine · 2026Article
- Fast, motion-robust MR elastography with distributed, generalized encoding.Magnetic resonance in medicine · 2025Article
- Combining the benefits of 3D acquisitions and spiral readouts for VASO fMRI at UHF.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Off-resonance artifact correction for MRI: A review.NMR in biomedicine · 2023Review
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2 authors.
Funding
Abstract
Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization techniques were computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. This paper considers 3D MRI with optional consideration of coil sensitivity, and addresses the multi-echo field map estimation and water-fat imaging problem. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state-of-the-art methods with similar memory requirements.
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