提出评估MRI模型准确性的新方法，发现常用ASL模型脑部可用、肾脏有偏差。
Analyzing Model Misspecification in Quantitative MRI: Application to Perfusion ASL
- 用统计检验法判断信号模型是否失配，基于极大似然估计和克拉美-罗界
- 在脑部验证模型有效，肾脏中发现中度模型失配
- 为定量MRI模型可靠性提供理论评估工具，适合医学影像研究者
定量MRI依赖于显式信号模型进行参数估计，但这些模型常受混杂影响且难以在体内验证。当假设的信号模型与真实数据生成过程不一致时，即为模型失配。在此情况下，任意无偏估计量的方差下限由失配克拉美-罗界（MCRB）决定，最大似然估计（MLE）可能产生偏差且不一致。本文基于此原理，提出两种检验方法：(i) 观察重复测量增加时经验MCRB是否趋近于标准克拉美-罗界（CRB）；(ii) 比较两个等大小子集的MLE估计值，评估其经验方差是否符合理论CRB预测。以动脉自旋标记（ASL）为例，结果表明常用ASL信号模型在脑部基本适用，在肾脏中存在中度失配。该框架为定量MRI中的模型有效性评估提供了通用且理论严谨的方法。
原文摘要 · Abstract (English)
Quantitative MRI (qMRI) involves parameter estimation governed by an explicit signal model. However, these models are often confounded and difficult to validate in vivo. A model is misspecified when the assumed signal model differs from the true data-generating process. Under misspecification, the variance of any unbiased estimator is lower-bounded by the misspecified Cramer-Rao bound (MCRB), and maximum-likelihood estimates (MLE) may exhibit bias and inconsistency. Based on these principles, we assess misspecification in qMRI using two tests: (i) examining whether empirical MCRB asymptotically approaches the CRB as repeated measurements increase; (ii) comparing MLE estimates from two equal-sized subsets and evaluating whether their empirical variance aligns with theoretical CRB predictions. We demonstrate the framework using arterial spin labeling (ASL) as an illustrative example. Our result shows the commonly used ASL signal model appears to be specified in the brain and moderately misspecified in the kidney. The proposed framework offers a general, theoretically grounded approach for assessing model validity in quantitative MRI.
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