TY - GEN
T1 - Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images
AU - Liu, Ziping
AU - Laforest, Richard
AU - Mhlanga, Joyce
AU - Fraum, Tyler J.
AU - Itani, Malak
AU - Dehdashti, Farrokh
AU - Siegel, Barry A.
AU - Jha, Abhinav K.
N1 - Funding Information:
Financial support for this work was provided by the Department of Biomedical Engineering and the Mallinckrodt Institute of Radiology at Washington University in St. Louis and an NVIDIA GPU grant. We also thank Qiye Tan for the help with developing the web-based app for the observer study.
Publisher Copyright:
Copyright © 2021 SPIE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - image quality assessment
KW - lung cancer
KW - observer study
KW - positron emission tomography
KW - simulation
UR - http://www.scopus.com/inward/record.url?scp=85101953645&partnerID=8YFLogxK
U2 - 10.1117/12.2582765
DO - 10.1117/12.2582765
M3 - Conference contribution
AN - SCOPUS:85101953645
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Samuelson, Frank W.
A2 - Taylor-Phillips, Sian
PB - SPIE
T2 - Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment
Y2 - 15 February 2021 through 19 February 2021
ER -