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Sartoretti T, Skawran S, Gennari AG, Maurer A, Euler A, Treyer V, Sartoretti E, Waelti S, Schwyzer M, von Schulthess GK, Burger IA, Huellner MW, Messerli M. Fully automated computational measurement of noise in positron emission tomography. Eur Radiol 2024; 34:1716-1723. [PMID: 37644149 PMCID: PMC10873217 DOI: 10.1007/s00330-023-10056-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/15/2023] [Accepted: 05/15/2023] [Indexed: 08/31/2023]
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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Affiliation(s)
- Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - André Euler
- University of Zurich, Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Elisabeth Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Stephan Waelti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Department of Radiology and Nuclear Medicine, Children's Hospital of Eastern Switzerland, St. Gallen, Switzerland
| | - Moritz Schwyzer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Health Sciences and Technology, Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Gustav K von Schulthess
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Irene A Burger
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Department of Nuclear Medicine, Kantonsspital Baden, Baden, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
- University of Zurich, Zurich, Switzerland.
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