Noise Considerations for PET Quantification Using Maximum and Peak Standardized Uptake Value

Unlabelled: In tumor response monitoring studies with (18)F-FDG PET, maximum standardized uptake value (SUV(max)) is commonly applied as a quantitative metric. Although it has several advantages due to its simplicity of determination, concerns about the influence of image noise on single-pixel SUV(max) persist. In this study, we measured aspects of bias and reproducibility associated with SUV(max) and the closely related peak SUV (SUV(peak)) using real patient data to provide a realistic noise context.

Methods: List-mode 3-dimensional PET data were acquired for 15 min over a single bed position in twenty (18)F-FDG oncology patients. For each patient, data were sorted so as to form 2 sets of images: respiration-gated images such that each image had statistical quality comparable to a 3 min/bed position scan, and 5 statistically independent (ungated) images of different durations (1, 2, 3, 4, and 5 min). Tumor SUV(max) and SUV(peak) (12-mm-diameter spheric region of interest positioned so as to maximize the enclosed average) were analyzed in terms of reproducibility and bias. The component of reproducibility due to statistical noise (independent of physiologic and other variables) was measured using paired SUVs from 2 comparable respiration-gated images. Bias was measured as a function of scan duration.

Results: Replicate tumor SUV measurements had a within-patient SD of 5.6% ± 0.9% for SUV(max) and 2.5% ± 0.4% for SUV(peak). SUV(max) had average positive biases of 30%, 18%, 12%, 4%, and 5% for the 1-, 2-, 3-, 4-, and 5-min images, respectively. SUV(peak) was also biased but to a lesser extent: 11%, 8%, 5%, 1%, and 4% for the 1-, 2-, 3-, 4-, and 5-min images, respectively.

Conclusion: The advantages of SUV(max) are best exploited when PET images have a high statistical quality. For images with noise properties typically associated with clinical whole-body studies, SUV(peak) provides a slightly more robust alternative for assessing the most metabolically active region of tumor.