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Updated: Jun 28, 2026

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
Published on: December 16, 2022
Application of three-class ROC analysis to task-based image quality assessment of simultaneous dual-isotope
Xin He1, Xiyun Song, Eric C Frey
1Department of Radiology, Johns Hopkins School of Medicine, 601 N. Caroline Street, Baltimore, MD 21287, USA. xinhe@jhmi.edu
Abstract:
The diagnosis of cardiac disease using dual-isotope myocardial perfusion SPECT (MPS) is based on the defect status in both stress and rest images, and can be modeled as a three-class task of classifying patients as having no, reversible, or fixed perfusion defects. Simultaneous acquisition protocols for dual-isotope MPS imaging have gained much interest due to their advantages including perfect registration of the (201)Tl and (99m)Tc images in space and time, increased patient comfort, and higher clinical throughput. As a result of simultaneous acquisition, however, crosstalk contamination, where photons emitted by one isotope contribute to the image of the other isotope, degrades image quality. Minimizing the crosstalk is important in obtaining the best possible image quality. One way to minimize the crosstalk is to optimize the injected activity of the two isotopes by considering the three-class nature of the diagnostic problem. To effectively do so, we have previously developed a three-class receiver operating characteristic (ROC) analysis methodology that extends and unifies the decision theoretic, linear discriminant analysis, and psychophysical foundations of binary ROC analysis in a three-class paradigm. In this work, we applied the proposed three-class ROC methodology to the assessment of the image quality of simultaneous dual-isotope MPS imaging techniques and the determination of the optimal injected activity combination. In addition to this application, the rapid development of diagnostic imaging techniques has produced an increasing number of clinical diagnostic tasks that involve not only disease detection, but also disease characterization and are thus multiclass tasks. This paper provides a practical example of the application of the proposed three-class ROC analysis methodology to medical problems.
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