Li Xin, Jha Abhinav K, Ghaly Michael, Elshahaby Fatma E A, Links Jonathan M, Frey Eric C
IEEE Trans Med Imaging. 2017 Apr;36(4):917-929. doi: 10.1109/TMI.2016.2643684. Epub 2016 Dec 22.
The Hotelling Observer (HO) is widely used to evaluate image quality in medical imaging. However, applying it to data that are not multivariate-normally (MVN) distributed is not optimal. In this paper, we apply two multi-template linear observer strategies to handle such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Next, a different linear observer template is estimated for and applied to each sub-ensemble. The first multi-template strategy, adapted from previous work, applies the HO to each sub-ensemble, calculates the area under the receiver operating characteristics curve (AUC) for each sub-ensemble, and averages the AUCs from all the sub-ensembles. The second strategy applies the Linear Discriminant (LD) to estimate test statistics for each sub-ensemble and calculates a single global AUC using the pooled test statistics from all the sub-ensembles. We show that this second strategy produces the maximum AUC when only shifting of the HO test statistics is allowed. We compared these strategies to the use of a single HO template for the entire data ensemble by applying them to the non-MVN data obtained from reconstructed images of a realistic simulated population of myocardial perfusion SPECT studies with the goal of optimizing the reconstruction parameters. Of the strategies investigated, the multi-template LD strategy yielded the highest AUC for any given set of reconstruction parameters. The optimal reconstruction parameters obtained by the two multi-template strategies were comparable and produced higher AUCs for each sub-ensemble than the single-template HO strategy.
霍特林观察者（HO）在医学成像中被广泛用于评估图像质量。然而，将其应用于非多元正态（MVN）分布的数据并非最优选择。在本文中，我们应用两种多模板线性观察者策略来处理此类数据。首先，将整个数据集划分为精确或近似MVN且同方差的子数据集。接下来，为每个子数据集估计并应用不同的线性观察者模板。第一种多模板策略改编自先前的工作，将HO应用于每个子数据集，计算每个子数据集的接收器操作特征曲线（AUC）下的面积，并对所有子数据集的AUC进行平均。第二种策略应用线性判别（LD）来估计每个子数据集的检验统计量，并使用所有子数据集的合并检验统计量计算单个全局AUC。我们表明，当仅允许HO检验统计量发生偏移时，第二种策略产生的AUC最大。我们将这些策略与对整个数据集使用单个HO模板的情况进行了比较，方法是将它们应用于从真实模拟的心肌灌注SPECT研究重建图像中获得的非MVN数据，目的是优化重建参数。在所研究的策略中，对于任何给定的一组重建参数，多模板LD策略产生的AUC最高。两种多模板策略获得的最优重建参数相当，并且每个子数据集产生的AUC都高于单模板HO策略。
