ArticleIEEE transactions on medical imaging2015
A majorize-minimize framework for Rician and non-central chi MR images.
Article in IEEE transactions on medical imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Dual-branch MRI denoising and motion artifact suppression with uncertainty estimation and segmentation-based evaluation.Medical physics · 2026Article
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- An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI.IEEE transactions on computational imaging · 2025Article
- MRI-scale histology validates spatial sensitivity of in-vivo MRI-based axon radius estimation.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Synthetic data in generalizable, learning-based neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Bias-reduced neural networks for parameter estimation in quantitative MRI.Magnetic resonance in medicine · 2024Article
- Diffusion tensor brain imaging at 0.55T: A feasibility study.Magnetic resonance in medicine · 2024Article
- BrainSuite BIDS App: Containerized Workflows for MRI Analysis.bioRxiv : the preprint server for biology · 2024Article
- The "hidden noise" problem in MR image reconstruction.Magnetic resonance in medicine · 2024Article
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- Correcting inter-scan motion artifacts in quantitative RMagnetic resonance in medicine · 2022Article
- Efficient iterative solutions to complex-valued nonlinear least-squares problems with mixed linear and antilinear operators.Optimization and engineering · 2022Article
- Article
- Local perturbation responses and checkerboard tests: Characterization tools for nonlinear MRI methods.Magnetic resonance in medicine · 2021Article
- Model-based multi-parameter mapping.Medical image analysis · 2021Article
- Training a neural network for Gibbs and noise removal in diffusion MRI.Magnetic resonance in medicine · 2021Article
- Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications.NMR in biomedicine · 2020Article
- Fast submillimeter diffusion MRI using gSlider-SMS and SNR-enhancing joint reconstruction.Magnetic resonance in medicine · 2020Article
- Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.Medical image analysis · 2019Article
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2 authors.
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Abstract
The statistics of many MR magnitude images are described by the non-central chi (NCC) family of probability distributions, which includes the Rician distribution as a special case. These distributions have complicated negative log-likelihoods that are nontrivial to optimize. This paper describes a novel majorize-minimize framework for NCC data that allows penalized maximum likelihood estimates to be obtained by solving a series of much simpler regularized least-squares surrogate problems. The proposed framework is general and can be useful in a range of applications. We illustrate the potential advantages of the framework with real and simulated data in two contexts: 1) MR image denoising and 2) diffusion profile estimation in high angular resolution diffusion MRI. The proposed approach is shown to yield improved results compared to methods that model the noise statistics inaccurately and faster computation relative to commonly-used nonlinear optimization techniques.
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