ArticleMagnetic resonance in medicine2026
Methods for Uncertainty Quantification in Dictionary Matching to Advance Reliability of Quantitative MRI.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimsPurpose: Dictionary matching is a standard tool in quantitative MRI (qMRI), but typically lacks uncertainty quantification (UQ). This is critical when advanced reconstructions (e.g., compressed sensing, deep learning) introduce complex-valued, spatially varying, and temporally correlated noise that violates standard assumptions of independent and identically distributed (iid) noise.
methodsTwo voxel-wise uncertainty methods: a frequentist Likelihood Ratio Test (LRT) and a Bayesian marginal posterior approach, are introduced. Noise is modeled as spatially varying and temporally correlated using the covariance estimated from background regions. Methods were validated via simulations and phantom experiments using radial turbo spin-echo (
resultsSimulations confirmed both methods achieve nominal coverage rates (e.g., 95% intervals containing the true value 95% of the time) where standard iid assumptions fail. Phantom results showed excellent agreement with gold-standard spin-echo references. In vivo experiments highlighted that higher acceleration factors widen uncertainty intervals for
conclusionA robust framework is presented for UQ in dictionary-matched qMRI. By modeling the non-iid noise inherent in modern reconstructions, these methods provide statistically interpretable UQ that assesses the reliability of parameter maps in clinical settings.
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