title: Radiographers' Ability to Detect Low-Contrast Detail in Digital Radiography Systems.
abstract: OBJECTIVE
  To evaluate radiographers' ability to detect low-contrast detail using various digital planar radiographic systems.
  
  
  METHODS
  A low-contrast detail phantom was placed between two 5-cm thick Perspex sheets (Lucite International). Images were obtained using different kilovoltage peak and milliamperage second (mAs) settings with computed radiography (CR), indirect conversion digital radiography (IDR), and direct conversion digital radiography (DR) systems. Six groups of 6 radiographers were asked to score 39 images; each group scored 2 images from each system for a total of 6 images. The seventh group scored only one image from each system for a total of 3 images. The radiographers' results were compared with the results of analyzer software. The inverse image quality factor was used to measure low-contrast detail detectability performance.
  
  
  RESULTS
  Radiographers performed significantly worse than the computerized software in determining low-contrast detail in planar radiographic images (P < .01). However, a positive correlation (R = 0.558) existed between the 2 sets of scores in terms of low-contrast detail detectability performance.
  
  
  CONCLUSIONS
  On average, radiographers were able to detect increased image quality resulting from increased mAs. Radiographers reached results similar to the software regarding whether IDR and DR have better detectability performances than CR. Differences found among individual radiographers were not as significant with DR.
  
  
  CONCLUSIONS
  When radiographers' performance in detecting low-contrast detail was evaluated and compared with that of the software, radiographers exhibited poorer performances. Because radiographers are responsible for image quality optimization, additional training might improve their ability to detect low-contrast detail in DR systems.
authors.forename: Haney
authors.initials: H
authors.lastname: Alsleem
authors.forename: Robert
authors.initials: R
authors.lastname: Davidson
mesh_terms: D002983:Clinical Competence / Q000706:statistics & numerical data*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D011871:Radiology / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014481:United States