title: Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology & Nuclear Medicine, VU University Medical Centre, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands, n.makris@vumc.nl.
authors.forename: Nikolaos E
authors.initials: NE
authors.lastname: Makris
authors.forename: Marc C
authors.initials: MC
authors.lastname: Huisman
authors.forename: Paul E
authors.initials: PE
authors.lastname: Kinahan
authors.forename: Adriaan A
authors.initials: AA
authors.lastname: Lammertsma
authors.forename: Ronald
authors.initials: R
authors.lastname: Boellaard
mesh_terms: D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging; D003627:Data Interpretation, Statistical; D004938:Esophageal Neoplasms / Q000000981:diagnostic imaging; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D015337:Multicenter Studies as Topic; D064847:Multimodal Imaging / Q000379:methods; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D015898:Tomography Scanners, X-Ray Computed; D014057:Tomography, X-Ray Computed / Q000295:instrumentation* / Q000379:methods
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18