ArticleFrontiers in nuclear medicine2024
Deep-learning-derived input function in dynamic [
Article in Frontiers in nuclear medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Assessment of early-phase [Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
- Image-derived input functions for [18F]LW223 and [18F]SynVesT-1 PET in the rodent determined using an autoencoder (IDIF-AE).Physics in medicine and biology · 2026Article
- Quantitative Preclinical Imaging as a Metrological Framework: Reproducibility, Validation, and Translational Maturity.Journal of imaging · 2026Review
- CXCR4-targeted PET imaging of glioblastoma using [EJNMMI radiopharmacy and chemistry · 2026Article
- Advances and Challenges in Pharmacokinetic Modeling for PET Imaging: Compartment Models, Input Functions, and Quantitative Techniques.Tomography (Ann Arbor, Mich.) · 2026Review
- A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [EJNMMI research · 2026Article
- Variability of the arterial input function in small-animal dynamic PET imaging.EJNMMI research · 2026Article
- Deep learning-derived arterial input function for dynamic brain PET.NeuroImage · 2025Article
- Neural network-aided unsupervised input function estimation for dual-time-window PET Patlak analysis.EJNMMI physics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [
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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.