PLoS One

Cumulative receiver operator characteristic (ROC) curve analysis extends classic ROC curve analysis to discriminate three or more ordinal outcome levels on a shared continuous scale. The procedure combines cumulative logit regression with a cumulative extension to the ROC curve and performs as expected with ternary (three-level) ordinal outcomes under a variety of simulated conditions (unbalanced data, proportional and non-proportional odds, areas under the ROC curve [AUCs] from 0.70 to 0.95). Simulations also compared several criteria for selecting cutpoints to discriminate outcome levels: the Youden Index, Matthews Correlation Coefficient, Total Accuracy, and Markedness. Total Accuracy demonstrated the least absolute percent-bias. Cutpoints computed from maximum likelihood regression parameters demonstrated bias that was often negligible. The procedure was also applied to publicly available data related to computer imaging and biomarker exposure science, yielding good to excellent AUCs, as well as cutpoints with sensitivities and specificities of commensurate quality. Implementation of cumulative ROC curve analysis and extension to more than three outcome levels are straightforward. The author's programs for ternary ordinal outcomes are publicly available.31469848PMC6716631786

TOWARDS FURTHER OPTIMIZATION OF RECONSTRUCTION METHODS FOR DUAL-RADIONUCLIDE MYOCARDIAL PERFUSION SPECT

Coronary artery disease (CAD) is the most prevalent type of heart disease and a leading cause of death both in the United States and worldwide. Myocardial perfusion SPECT (MPS) is a well-established and widely-used non-invasive imaging technique to diagnose CAD. MPS images the distribution of radioactive perfusion agent in the myocardium to assess the myocardial perfusion status at rest and stress state and allow diagnosis of CAD and allow differentiation of CAD and previous myocardial infarctions. The overall goal of this dissertation was to optimize the image reconstruction methods for MPS by patient-specific optimization of two advanced iterative reconstruction methods based on simulations of realistic patients population modeling existing hardware and previously optimized dual-isotope simultaneous-acquisition imaging protocols. After optimization, the two algorithms were compared to determine the optimal reconstruction methods for MPS. First, we developed a model observer strategy to evaluate image quality and allow optimization of the reconstruction methods using a population of phantoms modeling the variability seen in human populations. The Hotelling Observer (HO) is widely used to evaluate image quality, often in conjunction with anthropomorphic channels to model human observer performance. However, applying the HO to non- multivariate-normally (MVN) distributed, such as the output from a channel model applied to images with variable signals and background, is not optimal. In this work, we proposed a novel model observer strategy to evaluate the image quality of such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Next, the Linear Discriminant (LD) is applied to estimate test statistics for each sub-ensemble, and a single area under the receiver operating characteristics curve (AUC) is calculated using the pooled test statistics from all the sub-ensembles. The AUC serves as the figure of merit for performance on the defect detection task. The proposed multi-template LD was compared to other model observer strategies and was shown to be a practical, theoretically justified, and produced higher AUC values for non-MVN data such as that arising from the clinically-realistic SKS task used in the remainder of this work. We then optimized two regularized statistical reconstruction algorithms. One is the widely used post-filtered ordered subsets-expectation maximization (OS-EM) algorithm. The other is a maximum a posteriori (MAP) algorithm with dual-tracer prior (DTMAP) that was proposed for dual-isotope MPS study and was expected to outperform the post-filtered OS-EM algorithm. Of importance, we proposed to investigate patient-specific optimization of the reconstruction parameters. To accomplish this, the phantom population was divided into three anatomy groups based on metrics that expected to affect image noise and resolution and thus the optimal reconstruction parameters. In particular, these metrics were the distance from the center of the heart to the face of the collimator, which is directly related to image resolution, heart size, and counts from the myocardium, which is expected to determine image noise. Reconstruction parameters were optimized for each of these groups using the proposed model observer strategy. Parameters for the rest and stress images were optimized separately, and the parameters that achieve the highest AUC were deemed optimal. The results showed that the proposed group-wise optimization method offered slightly better task performance than using a single set of parameters for all the phantoms. For DTMAP, we also applied the group-wise optimization approach. The extra challenges for DTMAP optimization are that it has three parameters to be optimized simultaneously, and it is substantially more computationally expensive than OS-EM. Thus, we adopted optimization strategies to reduce the size of the parameter search space. In particular, we searched in two parameter ranges expected to give result in good image quality. We also reduced the computation burden by exploiting limiting behavior of the penalty function to reduce the number of parameters that need to be optimized. Despite this effort, the optimized DTMAP had poorer task performance compared to the optimized OS-EM algorithm. As a result, we studied the limitations of the DTMAP algorithm and suggest reasons of its worse performance for the task investigated. The results of this study indicate that there is benefit from patient-specific optimization. The methods and optimal patient-specific parameters may be applicable to clinical MPS studies. In addition, the model observer strategy and the group-wise optimization approach may also be applicable both to future work in MPS and to other relevant fields

Performance metrics for the evaluation of hyperspectral chemical identification systems

Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume’s chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.United States. Defense Threat Reduction Agency (Air Force contract FA8721-05-C-0002

強い重力レンズモデルの比較研究

Tohoku University二間瀬敏史課