ArticleIEEE transactions on bio-medical engineering2019
Nonlinear Mixed-Effects Models for PET Data.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Convergent increases in serotonin 1B receptor binding following ketamine and electroconvulsive therapy: a multi-centre bayesian re-analysis of PET data.Molecular psychiatry · 2026Article
- A reference tissue implementation of Simultaneous Multifactor Bayesian Analysis (SiMBA) of PET time activity curve data.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment modelPhysics in medicine and biology · 2024Article
- Nonlinear Mixed-Effects Modeling Approach for Simplified Reference Tissue Model.IEEE transactions on bio-medical engineering · 2024Article
- Kinetic models for estimating occupancy from single-scan PET displacement studies.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Article
- Multivariate analysis of PET pharmacokinetic parameters improves inferential efficiency.EJNMMI physics · 2023Article
- The Association of Folic Acid, Iron Nutrition during Pregnancy and Congenital Heart Disease in Northwestern China: A Matched Case-Control Study.Nutrients · 2022Article
- Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data.NeuroImage · 2022Article
- NRM 2021 Abstract Booklet.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- Early stopping in clinical PET studies: How to reduce expense and exposure.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Review
- Kinfitr - an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability.EJNMMI research · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
objectiveThe current state-of-the-art for compartment modeling of dynamic PET data can be described as a two-stage approach. In Stage 1, individual estimates of kinetic parameters are obtained by fitting models using standard techniques, such as nonlinear least squares, to each individual's data one subject at a time. Population-level effects, such as the difference between diagnostic groups, are analyzed in Stage 2 using standard statistical methods by treating the individual estimates as if they were observed data. While this approach is generally valid, it is possible to increase efficiency and precision of the analysis, allow more complex models to be fitted, and also to permit parameter-specific investigation by fitting data across subjects simultaneously. We explore the application of nonlinear mixed-effects (NLME) models for estimation and inference in this setting.
methodsIn the NLME framework, subjects are modeled simultaneously through the inclusion of random effects of subjects for each kinetic parameter; meanwhile, population parameters are estimated directly in a joint model.
resultsSimulation results indicate that NLME outperforms the two-stage approach in estimating group-level effects and also has improved power to detect differences across groups. We applied our NLME approach to clinical PET data and found effects not detected by the two-stage approach.
conclusionThe proposed NLME approach is more accurate and correspondingly more powerful than the two-stage approach in compartment modeling of PET data. SIGNIFICANCE: The NLME method can broaden the methodological scope of PET modeling because of its efficiency and stability.
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