Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

• URL: http://arxiv.org/abs/2608.11942v1
• Date: Wed, 12 Aug 2026 11:30:31 GMT
• Title: Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping
• Abstract summary: Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration.<n> Diffusion model-derived uncertainty was positively associated with the mapping error.<n>Raw uncertainty was poorly calibrated for quantitative interval interpretation.
• Score: 0.504092226811181
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not explicitly characterised. In this work we systematically evaluate uncertainty maps for quantitative MRI derived from multiple inferences of a data-consistent diffusion model-based qMRI framework. Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration. Diffusion model-derived uncertainty was positively associated with the mapping error, while risk-coverage analysis showed that excluding high-uncertainty voxels reduced the retained error. However, the raw uncertainty was poorly calibrated for quantitative interval interpretation. Calibration was substantially improved using a post-hoc procedure combining prediction-value-dependent bias correction with scalar uncertainty scaling. Qualitative evaluation on a healthy volunteer showed spatially meaningful uncertainty patterns. These results indicate that diffusion model-derived uncertainty is informative for reliability assessment and selective prediction, but requires calibration for quantitative interval interpretation.

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