Purpose: PET quantification based on standardized uptake values (SUV) is hampered by several factors, in particular by variability in PET acquisition settings and data analysis methods. Quantitative PET/CT studies acquired during a multicentre trial require harmonization of imaging procedures to maximize study power. The aims of this study were to determine which phantoms are most suitable for detecting differences in image quality and quantification, and which methods for defining volumes of interest (VOI) are least sensitive to these differences.
Methods: The most common accreditation phantoms used in oncology FDG PET/CT trials were scanned on the same scanner. These phantoms were those used by the Society of Nuclear Medicine Clinical Trials Network (SNM-CTN), the European Association of Nuclear Medicine/National Electrical Manufacturers Association (EANM/NEMA) and the American College of Radiology (ACR). In addition, tumour SUVs were derived from ten oncology whole-body examinations performed on the same PET/CT system. Both phantom and clinical data were reconstructed using different numbers of iterations, subsets and time-of-flight kernel widths. Subsequently, different VOI methods (VOI(A50%), VOI(max), VOI(3Dpeak), VOI(2Dpeak)) were applied to assess the impact of changes in image reconstruction settings on SUV and recovery coefficients (RC).
Results: All phantoms demonstrated sensitivity for detecting changes in SUV and RC measures in response to changes in image reconstruction settings and VOI analysis methods. The SNM-CTN and EANM/NEMA phantoms showed almost equal sensitivity in detecting RC differences with changes in image characteristics. Phantom and clinical data demonstrated that the VOI analysis methods VOI(A50%) and VOI(max) gave SUV and RC values with large variability in relation to image characteristics, whereas VOI(3Dpeak) and VOI(2Dpeak) were less sensitive to these differences.
Conclusion: All three phantoms may be used to harmonize parameters for data acquisition, processing and analysis. However, the SNM-CTN and EANM/NEMA phantoms are the most sensitive to parameter changes and are suitable for harmonizing SUV quantification based on 3D VOIs, such as VOI(A50%) and VOI(3Dpeak), and VOImax. Variability in SUV quantification after harmonization could be further minimized using VOI(3Dpeak) analysis, which was least sensitive to residual variability in image quality and quantification.