title: Evaluating image reconstruction methods for tumor detection in 3-dimensional whole-body PET oncology imaging.
abstract: We compare 3 image reconstruction algorithms for use in 3-dimensional (3D) whole-body PET oncology imaging. We have previously shown that combining Fourier rebinning (FORE) with 2-dimensional (2D) statistical image reconstruction via the ordered-subsets expectation-maximization (OSEM) and attenuation-weighted OSEM (AWOSEM) algorithms demonstrates improvements in image signal-to-noise ratios compared with the commonly used analytic 3D reprojection (3DRP) or FORE+FBP (2D filtered backprojection) reconstruction methods. To assess the impact of these reconstruction methods on detecting and localizing small lesions, we performed a human observer study comparing the different reconstruction methods. The observer study used the same volumetric visualization software tool that is used in clinical practice, instead of a planar viewing mode as is generally used with the standard receiver operating characteristic (ROC) methodology. This change in the human evaluation strategy disallowed the use of a ROC analysis, so instead we compared the fraction of actual targets found and reported (fraction-found) and also investigated the use of an alternative free-response operating characteristic (AFROC) analysis.
  
  
  METHODS
  We used a non-Monte Carlo technique to generate 50 statistically accurate realizations of 3D whole-body PET data based on an extended mathematic cardiac torso (MCAT) phantom and with noise levels typical of clinical scans performed on a PET scanner. To each realization, we added 7 randomly located 1-cm-diameter lesions (targets) whose contrasts were varied to sample the range of detectability. These targets were inserted in 3 organs of interest: lungs, liver, and soft tissues. The images were reconstructed with 3 reconstruction strategies (FORE+OSEM, FORE+AWOSEM, and FORE+FBP). Five human observers reported (localized and rated) 7 targets within each volume image. An observer's performance accuracy with each algorithm was measured, as a function of the lesion contrast and organ type, by the fraction of those targets reported and by the area below the AFROC curve. This AFROC curve plots the fraction of reported targets at each rating threshold against the fraction of cases with (> or =1) similarly rated false reports.
  
  
  RESULTS
  Images reconstructed with FORE+AWOSEM yielded the best overall target detection as compared with FORE+FBP and FORE+OSEM, although these differences in detectability were region specific. The FORE+FBP and FORE+AWOSEM algorithms had similar performances for liver targets. The FORE+OSEM algorithm performed significantly worse at target detection, especially in the liver. We speculate that this is the result of using an incorrect statistical model for OSEM and that the incorporation of attenuation weighting in AWOSEM largely compensates for this model inaccuracy. These results were consistent for both the fraction of actual targets found and the AFROC analysis.
  
  
  CONCLUSIONS
  We demonstrated the efficacy of performing observer detection studies using the same visualization tools as those used in clinical PET oncology imaging. These studies demonstrated that the FORE+AWOSEM algorithm led to the best overall detection and localization performance for 1-cm-diameter targets compared with the FORE+OSEM and FORE+FBP algorithms.
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Carole
authors.initials: C
authors.lastname: Lartizien
authors.forename: Paul E
authors.initials: PE
authors.lastname: Kinahan
authors.forename: Richard
authors.initials: R
authors.lastname: Swensson
authors.forename: Claude
authors.initials: C
authors.lastname: Comtat
authors.forename: Michael
authors.initials: M
authors.lastname: Lin
authors.forename: Victor
authors.initials: V
authors.lastname: Villemagne
authors.forename: Régine
authors.initials: R
authors.lastname: Trébossen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D009369:Neoplasms / Q000000981:diagnostic imaging*; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012983:Soft Tissue Neoplasms / Q000000981:diagnostic imaging; D013269:Stochastic Processes; D014055:Tomography, Emission-Computed / Q000379:methods*; D014915:Whole-Body Counting / Q000379:methods