title: The performance of projective standardization for digital subtraction radiography.
abstract: OBJECTIVE
  We sought to test the performance and robustness of projective standardization in preserving invariant properties of subtraction images in the presence of irreversible projection errors. Study design Twenty bone chips (1-10 mg each) were placed on dentate dry mandibles. Follow-up images were obtained without the bone chips, and irreversible projection errors of up to 6 degrees were introduced. Digitized image intensities were normalized, and follow-up images were geometrically reconstructed by 2 operators using anatomical and fiduciary landmarks. Subtraction images were analyzed by 3 observers.
  
  
  RESULTS
  Regression analysis revealed a linear relationship between radiographic estimates of mineral loss and actual mineral loss (R(2) = 0.99; P <.05). The effect of projection error was not significant (general linear model [GLM]: P >.05). There was no difference between the radiographic estimates from images standardized with anatomical landmarks and those standardized with fiduciary landmarks (Wilcoxon signed rank test: P >.05). Operator variability was low for image analysis alone (R(2) = 0.99; P <.05), as well as for the entire procedure (R(2) = 0.98; P <.05). The predicted detection limit was smaller than 1 mg.
  
  
  CONCLUSIONS
  Subtraction images registered by projective standardization yield estimates of osseous change that are invariant to irreversible projection errors of up to 6 degrees. Within these limits, operator precision is high and anatomical landmarks can be used to establish correspondence.
authors.affiliation: University of North Carolina, Chapel Hill, NC 27599-7450, USA. Andre_Mol@dentistry.unc.edu
authors.forename: André
authors.initials: A
authors.lastname: Mol
authors.forename: Stanley M
authors.initials: SM
authors.lastname: Dunn
mesh_terms: D000465:Algorithms; D016301:Alveolar Bone Loss / Q000000981:diagnostic imaging; D015519:Bone Density; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008334:Mandible / Q000000981:diagnostic imaging; D015588:Observer Variation; D011856:Radiographic Image Enhancement / Q000592:standards*; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric; D013382:Subtraction Technique / Q000592:standards*; D014962:X-Ray Film