Bayesian Uncertainty-Aware MRI Reconstruction

• URL: http://arxiv.org/abs/2603.13439v1
• Date: Fri, 13 Mar 2026 11:14:53 GMT
• Title: Bayesian Uncertainty-Aware MRI Reconstruction
• Abstract summary: We propose a novel framework for joint magnetic resonance image reconstruction using under-sampled k-space measurements.<n>Our framework effectively quantifies uncertainty, showing strong correlation with error maps computed from reconstructed and ground-truth images.
• Score: 5.452868777437004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 proposed framework over optimisation-based compressed sensing algorithms. Additionally, our framework effectively quantifies uncertainty, showing strong correlation with error maps computed from reconstructed and ground-truth images.

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