The PET Core Laboratory of the American College of Radiology Imaging Network (ACRIN) qualifies sites to participate in multicenter research trials by quantitatively reviewing submitted PET scans of uniform cylinders to verify the accuracy of scanner standardized uptake value (SUV) calibration and qualitatively reviewing clinical PET images from each site. To date, cylinder and patient data from 169 PET scanners have been reviewed, and 146 have been qualified.
Methods: Each site is required to submit data from 1 uniform cylinder and 2 patient test cases. Submitted phantom data are analyzed by drawing a circular region of interest that encompasses approximately 90% of the diameter of the interior of the phantom and then recording the mean SUV and SD of each transverse slice. In addition, average SUVs are measured in the liver of submitted patient scans. These data illustrate variations of SUVs across PET scanners and across institutions, and comparison of results with values submitted by the site indicate the level of experience of PET camera operators in calculating SUVs.
Results: Of 101 scanner applications for which detailed records of the qualification process were available, 12 (12%) failed because of incorrect SUV or normalization calibrations. For sites to pass, the average cylinder SUV is required to be 1.0 +/- 0.1. The average SUVs for uniform cylinder images for the most common scanners evaluated-Siemens Biograph PET/CT (n = 43), GE Discovery LS PET/CT (n = 15), GE Discovery ST PET/CT (n = 34), Philips Allegro PET (n = 5), and Philips Gemini PET/CT (n = 11)-were 0.99, 1.01, 1.00, 0.98, and 0.95, respectively, and the average liver SUVs for submitted test cases were 2.34, 2.13, 2.27, 1.73, and 1.92, respectively.
Conclusion: Minimizing errors in SUV measurement is critical to achieving accurate quantification in clinical trials. The experience of the ACRIN PET Core Laboratory shows that many sites are unable to maintain accurate SUV calibrations without additional training or supervision. This raises concerns about using SUVs to quantify patient data without verification.