title: Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise.
abstract: OBJECTIVE
  The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise.
  
  
  METHODS
  Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions.
  
  
  RESULTS
  Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001).
  
  
  CONCLUSIONS
  In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.
authors.affiliation: Department of Radiology, University of Washington School of Medicine, Seattle, WA 98195, USA. wshuman@u.washington.edu
authors.forename: William P
authors.initials: WP
authors.lastname: Shuman
authors.forename: Doug E
authors.initials: DE
authors.lastname: Green
authors.forename: Janet M
authors.initials: JM
authors.lastname: Busey
authors.forename: Orpheus
authors.initials: O
authors.lastname: Kolokythas
authors.forename: Lee M
authors.initials: LM
authors.lastname: Mitsumori
authors.forename: Kent M
authors.initials: KM
authors.lastname: Koprowicz
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Thibault
authors.forename: Jiang
authors.initials: J
authors.lastname: Hsieh
authors.forename: Adam M
authors.initials: AM
authors.lastname: Alessio
authors.forename: Eunice
authors.initials: E
authors.lastname: Choi
authors.forename: Paul E
authors.initials: PE
authors.lastname: Kinahan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*