Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images

Ziping Liu; Richard Laforest; Joyce Mhlanga; Tyler J. Fraum; Malak Itani; Farrokh Dehdashti; Barry A. Siegel; Abhinav K. Jha

doi:10.1117/12.2582765

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15 February 2021 Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images

Ziping Liu, Richard Laforest, Joyce Mhlanga, Tyler J. Fraum, Malak Itani, Farrokh Dehdashti, Barry A. Siegel, Abhinav K. Jha

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Proceedings Volume 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment; 1159905 (2021) https://doi.org/10.1117/12.2582765
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Objective evaluation of new and improved methods for PET imaging requires access to images with ground truth, as can be obtained through simulation studies. However, for these studies to be clinically relevant, it is important that the simulated images are clinically realistic. In this study, we develop a stochastic and physics-based method to generate realistic oncological two-dimensional (2-D) PET images, where the ground-truth tumor properties are known. The developed method extends upon a previously proposed approach. The approach captures the observed variabilities in tumor properties from actual patient population. Further, we extend that approach to model intra-tumor heterogeneity using a lumpy object model. To quantitatively evaluate the clinical realism of the simulated images, we conducted a human-observer study. This was a two-alternative forced-choice (2AFC) study with trained readers (five PET physicians and one PET physicist). Our results showed that the readers had an average of ∼ 50% accuracy in the 2AFC study. Further, the developed simulation method was able to generate wide varieties of clinically observed tumor types. These results provide evidence for the application of this method to 2-D PET imaging applications, and motivate development of this method to generate 3-D PET images.

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Ziping Liu, Richard Laforest, Joyce Mhlanga, Tyler J. Fraum, Malak Itani, Farrokh Dehdashti, Barry A. Siegel, and Abhinav K. Jha "Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images", Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 1159905 (15 February 2021); https://doi.org/10.1117/12.2582765

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