Bayesian Uncertainty-Aware MRI Reconstruction
We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as a Bayesian linear inverse problem, where prior distributions are assigned to the unknown model parameters. Specifically, we assume the target image is sparse in its spatial gradient and impose a total variation prior model. A Markov chain Monte Carlo (MCMC) method, based on a split-and-augmented Gibbs sampler, is then used to sample from the resulting joint posterior distribution of the unknown parameters. Experiments conducted using single- and multi-coil datasets demonstrate the superior performance of the pr...
| Authors | Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander |
|---|---|
| Primary category | eess.IV |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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