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Open Access 30-08-2023 | Positron Emission Tomography | Molecular Imaging

Fully automated computational measurement of noise in positron emission tomography

Authors: Thomas Sartoretti, Stephan Skawran, Antonio G. Gennari, Alexander Maurer, André Euler, Valerie Treyer, Elisabeth Sartoretti, Stephan Waelti, Moritz Schwyzer, Gustav K. von Schulthess, Irene A. Burger, Martin W. Huellner, Michael Messerli

Published in: European Radiology | Issue 3/2024

Abstract

Objectives

To introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.

Methods

[¹⁸F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI.

Results

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.

Conclusion

An 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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Title: Fully automated computational measurement of noise in positron emission tomography
Authors: Thomas Sartoretti
Stephan Skawran
Antonio G. Gennari
Alexander Maurer
André Euler
Valerie Treyer
Elisabeth Sartoretti
Stephan Waelti
Moritz Schwyzer
Gustav K. von Schulthess
Irene A. Burger
Martin W. Huellner
Michael Messerli
Publication date: 30-08-2023
Publisher: Springer Berlin Heidelberg
Keyword: Positron Emission Tomography
Published in: European Radiology / Issue 3/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-10056-w

2024 ASCO Annual Meeting

Springer Medicine

Fully automated computational measurement of noise in positron emission tomography

Abstract

Objectives

Methods

Results

Conclusion

Clinical relevance statement

Key Points

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Clinical relevance statement

Key Points

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