ArticleEJNMMI research2026
Variability of the arterial input function in small-animal dynamic PET imaging.
Article in EJNMMI research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [EJNMMI research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDynamic positron emission tomography (PET) combined with tracer kinetic modeling enables non-invasive quantification of biochemical processes. A prerequisite is availability of the arterial input function (AIF), which, in small-animal PET imaging, involves labor-intensive terminal surgery. Deep learning based input function (DLIF) allows estimation of whole-blood tracer concentration from PET data, avoiding arterial cannulation, but require comprehensive validation. The aim of this study was to collect dynamic PET and AIF data under controlled conditions and to evaluate the variability of AIF, image-derived input function (IDIF), and kinetic modeling parameters, which is important for future DLIF model training. Dynamic PET and AIF data were collected prospectively from 112 mice in groups with varying experimental conditions, including radiotracer injection volume, injection time, withdrawal rate, mouse age, strain, radiopharmaceutical, and PET scanner. Brain, myocardium, left ventricle and liver were delineated for kinetic modeling and IDIF generation. Curve features and kinetic modeling parameters were computed, using both AIF and IDIF, and compared across groups using box plots and statistical tests. Intra-subject repeatability was evaluated in six mice using three small-volume radiotracer injections.
resultsExperimental factors such as mouse strain, injection time, withdrawal rate, PET scanner and radiopharmaceutical significantly affect AIF and IDIF shapes, while injection volume and mouse age, did not introduce bias. AIF measurements within the same subject were highly repeatable.
conclusionsThis study collected a comprehensive dataset of dynamic PET and AIF measurements under controlled conditions to evaluate the variability of AIF, IDIF, and kinetic modeling parameters. The findings provide valuable insights into input function variability, with potential implications for the future development of DLIF models across diverse experimental conditions.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.