title: Use of Sub-Ensembles and Multi-Template Observers to Evaluate Detection Task Performance for Data That are Not Multivariate Normal.
abstract: 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.
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.forename: Abhinav K
authors.initials: AK
authors.lastname: Jha
authors.forename: Michael
authors.initials: M
authors.lastname: Ghaly
authors.forename: Fatma E A
authors.initials: FE
authors.lastname: Elshahaby
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Links
authors.forename: Eric C
authors.initials: EC
authors.lastname: Frey
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D007091:Image Processing, Computer-Assisted; D013647:Task Performance and Analysis; D015899:Tomography, Emission-Computed, Single-Photon