ArticleEJNMMI research2025
Machine learning to identify suitable boundaries for band-pass spectral analysis of dynamic [
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 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Is there already value in "total body PET" imaging in neurology?The British journal of radiology · 2026Review
- Inter-individual variability of neurotransmitter receptor and transporter density in the human brain.Brain structure & function · 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
9 authors.
Funding
Abstract
backgroundSpectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injection. Band-pass spectral analysis, considering specific frequency ranges, enables calculation of separate parametric maps of receptor subtype tracer binding for suitable radiopharmaceuticals such as [
resultsThe best machine learning model, Fine Tree, agreed with the manual frequency boundary in 96.92% of 3185 ROIs. The absolute mean error was 3.80% for slow component volume-of-distribution (
conclusionThe machine learning model developed provided accurate boundary predictions in 96.92% of regions, with minimal average bias. However, when errors occur, they can be large, owing to the sparsity of peaks. The model enables setting boundaries automatically for the vast majority of regions, followed by manual checking of the outliers. It opens the possibility of accelerating analyses e.g. of GABA
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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 OpenQuestion graph.