Abstract
PurposeThe 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.MethodsAutomatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 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 spillover 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.ResultsThe 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 (< 1 ml).ConclusionCNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting.
Highlights
The standardized uptake value (SUV) is currently still the de facto standard for quantitative evaluation in clinicalElectronic supplementary material The online version of this article contains supplementary material, which is available to authorized users
The trained U-Net yields blood SUV (BSUV) in close agreement with those obtained from manual delineation
convolutional neural network (CNN) are capable of performing robust automatic image-based BSUV determination
Summary
It was shown that the uptake time normalized tumor to blood SUV ratio (standardized uptake ratio, SUR) essentially removes most of these shortcomings which leads to a distinctly improved correlation of this modified uptake measure with the metabolic uptake rate [4,5,6]. This in turn leads to improved test-retest stability [7] and significantly better prognostic value compared with tumor SUV [8,9,10,11]
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