Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imaging data. The problem is formulated as a linear inverse problem, where prior distributions are assigned to the unknown image parameters. Specifically, the image is assumed to be sparse in a given transform domain. We develop a general framework applicable to any sparsifying transform and demonstrate its performance using (1) a total variation transform based on image spatial gradients and (2) a wavelet-domain transform. Bayesian inference is performed using a split-and-augmented Gibbs sampler, while the resulting non-differentiable conditional ...
| Authors | Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander |
|---|---|
| Primary category | cs.CV |
| Published | 15 June 2026 |
| Last updated at source | 21 June 2026 |
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