title: Lung nodule detection in pediatric chest CT: quantitative relationship between image quality and radiologist performance.
abstract: OBJECTIVE
  To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT.
  
  
  METHODS
  The study included clinical chest CT images of 30 pediatric patients (0-16 years) scanned at tube currents of 55-180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7-32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3-5 mm and peak contrasts of 200-500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display).
  
  
  RESULTS
  AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU.
  
  
  CONCLUSIONS
  We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]
authors.affiliation: Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Ehsan
authors.initials: E
authors.lastname: Samei
authors.forename: Huiman X
authors.initials: HX
authors.lastname: Barnhart
authors.forename: Ana Maria
authors.initials: AM
authors.lastname: Gaca
authors.forename: Caroline L
authors.initials: CL
authors.lastname: Hollingsworth
authors.forename: Charles M
authors.initials: CM
authors.lastname: Maxfield
authors.forename: Caroline W T
authors.initials: CW
authors.lastname: Carrico
authors.forename: James G
authors.initials: JG
authors.lastname: Colsher
authors.forename: Donald P
authors.initials: DP
authors.lastname: Frush
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D015588:Observer Variation; D011361:Professional Competence; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*