title: Image comparative assessment using iterative reconstructions: clinical comparison of low-dose abdominal/pelvic computed tomography between adaptive statistical, model-based iterative reconstructions and traditional filtered back projection in 65 patients.
abstract: OBJECTIVE
  The objective of this study was to compare image quality (objective and subjective parameters) and confidence in lesion detection between 3 image reconstruction algorithms in computed tomographic (CT) examinations of the abdomen/pelvis.
  
  
  METHODS
  This prospective institutional review board-approved study included 65 patients (mean [SD] age, 71.3 ± 9 years; mean [SD] body mass index, 24.4 [4.8] kg) who underwent routine CT examinations of the abdomen/pelvis followed immediately by 2 low-dose scans. Raw data sets were reconstructed by using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and a model-based iterative reconstruction (MBIR). Measurements of objective noise and CT numbers were compared using repeated-measures analysis of variance. Six subjective image quality parameters were scored. Diagnostic confidence and accuracy in detection of various elementary lesions were performed.
  
  
  RESULTS
  Objectively, mean image noise for MBIR was significantly superior at all dose levels (P < 0.001). Subjectively, standard-dose ASIR and low-dose MBIR scans were better than standard-dose FBP scan in all parameters assessed (P < 0.05). Low-dose MBIR scans were comparable with standard-dose ASIR scans in all parameters except at noise index of 70 (approximately 85% dose reduction), where, in this case, the detection of liver lesions less than 5 mm were rated inferior (P < 0.05) with diagnostic accuracy reducing to 77.4%.
  
  
  CONCLUSIONS
  Low-dose MBIR scan shows superior objective noise reduction compared with standard-dose FBP and ASIR. Subjectively, low-dose MBIR scans at 76% dose reduction were also superior compared with standard-dose FBP and ASIR. However, at dose reductions of 85%, small liver lesions may be missed.
authors.affiliation: From the *Department of Radiology, Derriford Hospital; †Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth; and ‡University of Exeter Medical School, Exeter, United Kingdom.
authors.forename: Varut
authors.initials: V
authors.lastname: Vardhanabhuti
authors.forename: Richard D
authors.initials: RD
authors.lastname: Riordan
authors.forename: Grant R
authors.initials: GR
authors.lastname: Mitchell
authors.forename: Christopher
authors.initials: C
authors.lastname: Hyde
authors.forename: Carl A
authors.initials: CA
authors.lastname: Roobottom
mesh_terms: D000008:Abdominal Neoplasms / Q000000981:diagnostic imaging*; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D010386:Pelvic Neoplasms / Q000000981:diagnostic imaging*; D010388:Pelvis / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*