用扩散模型评估MRI定量映射的不确定性，提升结果可靠性。
Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping
- 基于扩散模型生成不确定性图，反映映射误差分布。
- 校准后不确定性可准确识别高误差区域，降低残留误差。
- 适合需要可信度评估的医学影像分析人员使用。
定量MRI（qMRI）提供标准化组织参数图，但基于深度学习的qMRI方法可靠性常缺乏明确表征。本文系统评估了基于数据一致性扩散模型框架的多次推断所得不确定性图。在合成测试数据上，评估了误差感知性、高误差检测能力、选择性预测及高斯区间校准性能。结果显示，扩散模型生成的不确定性与映射误差正相关；风险-覆盖率分析表明，剔除高不确定性体素可减少保留误差。然而原始不确定性在定量区间解释中校准不佳。通过结合预测值相关偏差修正与标量不确定性缩放的后处理方法，校准效果显著提升。健康志愿者的定性评估显示不确定性模式具有空间合理性。结果表明，扩散模型衍生的不确定性对可靠性评估和选择性预测具信息价值，但需校准以用于定量区间解释。
原文摘要 · Abstract (English)
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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