| Autor: |
Nikulin, P., Hofheinz, F., Maus, J., Li, Y., Bütof, R., Lange, C., Furth, C., Zschaeck, S., Kreissl, M. C., Kotzerke, J., Hoff, J. |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
European Journal of Nuclear Medicine and Molecular Imaging 48(2021), 995-1004 |
| Popis: |
Purpose: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor’s glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods: Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spill-over from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results: The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean ± SD) = (-0.5± 2.2)% with a 95% confidence interval of [−5.1, 3.8]% and a total range of [-10.0, 12.0]%. For four test cases the derived ROIs were unusable ( |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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