title: Impact of model-based iterative reconstruction on image quality of contrast-enhanced neck CT.
abstract: OBJECTIVE
  Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck.
  
  
  METHODS
  Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1-5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed.
  
  
  RESULTS
  Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 (P = .016), though there was no difference at level 2 (P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx (P < .001) and oropharynx (P < .001) and for overall image quality (P < .001) and was scored lower at the vocal cords (P < .001) and sternoclavicular junction (P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction.
  
  
  CONCLUSIONS
  Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction.
authors.affiliation: From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington sg272@uw.edu.
authors.forename: S
authors.initials: S
authors.lastname: Gaddikeri
authors.affiliation: From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington.
authors.forename: J B
authors.initials: JB
authors.lastname: Andre
authors.affiliation: Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington.
authors.forename: J
authors.initials: J
authors.lastname: Benjert
authors.affiliation: Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
authors.forename: D S
authors.initials: DS
authors.lastname: Hippe
authors.affiliation: From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington.
authors.forename: Y
authors.initials: Y
authors.lastname: Anzai
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods; D055815:Young Adult