Monte Carlo evaluation of hypothetical long axial field‐of‐view PET scanner using GE discovery MI PET front‐end architecture

2021 ◽  
Author(s):  
Ashok Tiwari ◽  
Michael Merrick ◽  
Stephen A. Graves ◽  
John Sunderland
Keyword(s):  
Monte Carlo ◽  
Field Of View ◽  
Axial Field ◽  
Front End ◽  
Monte Carlo Evaluation

Physical performance of a long axial field‐of‐view PET scanner prototype with sparse rings configuration: A Monte Carlo simulation study

2020 ◽  
Vol 47 (4) ◽  
pp. 1949-1957 ◽  
Author(s):  
Sara A. Zein ◽  
Nicolas A. Karakatsanis ◽  
Mohammad Issa ◽  
Amin A. Haj‐Ali ◽  
Sadek A. Nehmeh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Physical Performance ◽  
Simulation Study ◽  
Field Of View ◽  
Axial Field ◽  
Monte Carlo Simulation Study

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

A Monte Carlo evaluation of Bobko's ordinal interaction analysis technique.

1989 ◽  
Vol 74 (2) ◽  
pp. 242-246 ◽  
Author(s):  
Jeffrey Lee Rasmussen
Keyword(s):  
Monte Carlo ◽  
Interaction Analysis ◽  
Analysis Technique ◽  
Monte Carlo Evaluation

Monte Carlo evaluation of GaN THz Gunn diodes

2021 ◽  
Author(s):  
Wen Zhao Lee ◽  
Duu Sheng Ong ◽  
Kan Yeep Choo ◽  
Oktay Yilmazoglu ◽  
Hans L. Hartnagel
Keyword(s):  
Monte Carlo ◽  
Gunn Diodes ◽  
Monte Carlo Evaluation

Development of a deep learning method for CT-free attenuation correction for an ultra-long axial field of view PET scanner

2021 ◽  
Author(s):  
S Xue ◽  
KP Bohn ◽  
R Guo ◽  
H Sari ◽  
M Viscione ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attenuation Correction ◽  
Field Of View ◽  
Learning Method ◽  
Axial Field
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