定量磁共振成像中模型不规范性的分析：应用于灌注ASL
Analyzing Model Misspecification in Quantitative MRI: Application to Perfusion ASL
AI总结：
本文提出了一种评估定量磁共振成像中模型规范性的理论框架，通过两种测试验证ASL信号模型在不同组织中的规范性。
摘要：
定量磁共振成像（qMRI）涉及由显式信号模型指导的参数估计。然而，这些模型在体内往往受到混淆并难以验证。当假设的信号模型与真实的数据生成过程不同时，模型即为不规范。在不规范情况下，任何无偏估计量的方差下限由不规范Cramer-Rao界（MCRB）给出，最大似然估计（MLE）可能会表现出偏差和不一致性。基于这些原理，我们利用两种测试评估qMRI中的不规范性：（i）检查经验MCRB在重复测量增加时是否趋近于CRB；（ii）比较来自两个等大小子集的MLE估计值，并评估其经验方差是否符合理论CRB预测。我们通过动脉自旋标记（ASL）作为示例展示了该框架。我们的结果表明，常用的ASL信号模型在大脑中似乎规范，在肾脏中则中等不规范。所提出的框架提供了一种通用且理论基础的评估定量磁共振成像中模型有效性的方法。
英文摘要：
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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