title: Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction.
abstract: OBJECTIVE
  To qualitatively and quantitatively compare abdominal computed tomography (CT) images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0.
  
  
  METHODS
  This retrospective study was approved by our institutional review board and was Health Insurance Portability and Accountability Act compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75-mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference (RP), and Veo 3.0 20% RP. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions.The images were reviewed by 3 independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale.Multiple 2-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation.
  
  
  RESULTS
  The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference, 0.43; 95% confidence interval [95% CI], 0.25-0.6; P < 0.0001), overall image quality (mean difference, 0.87; 95% CI, 0.62-1.13; P < 0.0001) and qualitative resolution (mean difference, 0.9; 95% CI, 0.69-1.1; P < 0.0001). Although the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest.
  
  
  CONCLUSIONS
  Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations, such as focal lesion detection, in the oncology setting.
authors.affiliation: From the *Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center; †Department of Diagnostic Radiology, Baylor College of Medicine; ‡Imaging Physics, §Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX; and ∥Molecular Imaging and Computed Tomography Research, GE Healthcare, Waukesha, WI.
authors.forename: Corey T
authors.initials: CT
authors.lastname: Jensen
authors.forename: Morgan E
authors.initials: ME
authors.lastname: Telesmanich
authors.forename: Nicolaus A
authors.initials: NA
authors.lastname: Wagner-Bartak
authors.forename: Xinming
authors.initials: X
authors.lastname: Liu
authors.forename: John
authors.initials: J
authors.lastname: Rong
authors.forename: Janio
authors.initials: J
authors.lastname: Szklaruk
authors.forename: Aliya
authors.initials: A
authors.lastname: Qayyum
authors.forename: Wei
authors.initials: W
authors.lastname: Wei
authors.forename: Adam G
authors.initials: AG
authors.lastname: Chandler
authors.forename: Eric P
authors.initials: EP
authors.lastname: Tamm
mesh_terms: D000008:Abdominal Neoplasms / Q000000981:diagnostic imaging*; D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D011897:Random Allocation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult