Fully automated computational measurement of noise in positron emission tomography.
Algorithms
Dose reduction
Image enhancement
Noise
Positron emission tomography
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
30 Aug 2023
30 Aug 2023
Historique:
received:
09
12
2022
accepted:
15
05
2023
revised:
15
04
2023
medline:
30
8
2023
pubmed:
30
8
2023
entrez:
29
8
2023
Statut:
aheadofprint
Résumé
To introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets. [ Irrespective 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. An 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 of clinical PET imaging within and across institutions. • 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.
Identifiants
pubmed: 37644149
doi: 10.1007/s00330-023-10056-w
pii: 10.1007/s00330-023-10056-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s).
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