ArticleEuropean radiology2024
Fully automated computational measurement of noise in positron emission tomography.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Reconstruction-dependent differences in brain [18F]FDG PET: a large-scale same-acquisition comparison of Q.Clear and VUE Point FX.Annals of nuclear medicine · 2026Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT.EJNMMI research · 2025Article
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13 authors.
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Abstract
objectivesTo introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.
methods[
resultsIrrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600.
conclusionAn algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets. CLINICAL RELEVANCE STATEMENT: An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions. KEY POINTS: • Noise is an important quantitative marker that strongly impacts image quality of PET images. • An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets. • An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.
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