发表机构
Erasmus MC; TU Delft（伊拉斯姆斯医学中心; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对深度学习定量MRI成像方法可靠性未明确表征的问题，评估了扩散模型衍生的不确定性，发现其与成像误差正相关，经校准后可用于可靠性评估与选择性预测。
AI 中文摘要
定量MRI（qMRI）可提供标准化的组织参数图谱，但基于深度学习的qMRI成像方法的可靠性往往未被明确表征。本研究系统评估了基于数据一致性扩散模型的qMRI框架多次推理得到的定量MRI不确定性图谱。在合成测试数据上的评估涵盖误差感知、高误差检测、选择性预测及高斯区间校准。扩散模型衍生的不确定性与成像误差呈正相关，风险-覆盖分析显示，排除高不确定性体素可降低保留的误差。然而，原始不确定性对定量区间解释的校准效果较差。采用结合预测值相关偏差校正与标量不确定性缩放的事后程序，校准效果显著提升。对健康志愿者的定性评估显示出具有空间意义的不确定性模式。这些结果表明，扩散模型衍生的不确定性对可靠性评估和选择性预测具有参考价值，但需经校准方可用于定量区间解释。
英文摘要
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.
Comments11 pages, 6 figures. Accepted at the MICCAI 2026 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE 2026)
