ArticleEJNMMI research2025
Model selection for dynamic PET compartmental modelling of
Article in EJNMMI research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Kinetic modeling with total body PET-current status and future applications.The British journal of radiology · 2026Review
- Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.Zeitschrift fur medizinische Physik · 2026Review
- Subtype-Specific Metabolic Patterns in Staging Breast Cancer: Insights from Conventional and Parametric PET Radiomics.Cancers · 2026Article
- Parametric analysis of dynamic long axial field-of-viewEJNMMI physics · 2026Article
- Illuminating Glucose: How to Unveil Organ-Specific Insulin Resistance and Guide Metabolic Strategies in Diabetes.Diabetes/metabolism research and reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundIn dynamic PET with tracer kinetic modeling, model complexity is an important but often under-recognised challenge affecting robust parameter estimation, particularly for noisy data. Traditional methods often neglect tissue heterogeneity and apply a single model universally. We applied a model selection approach alongside delay and motion correction, enabling the selection of models with varying complexity to better account for tissue heterogeneity.
resultsThe study included five subjects with breast cancer undergoing dynamic
conclusionsWe applied a standard model selection approach to identify the optimal compartmental model for voxel-wise parameter estimation in long field-of-view dynamic PET imaging. Our results demonstrate that accounting for tissue heterogeneity in breast lesions is critical for accurate quantification. Additionally, delay and motion correction were shown to improve image quality, enhance quantification accuracy, and support more reliable model selection. CLINICAL
trial registrationClinical trial number: not applicable.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.