title: Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR).
abstract: OBJECTIVE
  The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR).
  
  
  METHODS
  In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent).
  
  
  RESULTS
  MBIR decreased artefacts more effectively than ASiR (p < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 (p < 0.01). SNR and CNR were significantly higher in MBIR than ASiR (p < 0.01).
  
  
  CONCLUSIONS
  MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics.
  
  
  CONCLUSIONS
  • Model-Based iterative reconstruction (MBIR) effectively decreased artefacts in cranial CT. • MBIR reconstructed images were rated with significantly higher scores for image quality. • Model-Based iterative reconstruction may allow reduced-dose diagnostic examination protocols.
authors.affiliation: Institute for Clinical Radiology, Ludwig-Maximilians University Hospital of Munich, Nussbaumstr. 20, 80336, Munich, Germany, Susan.Notohamiprodjo@med.uni-muenchen.de.
authors.forename: S
authors.initials: S
authors.lastname: Notohamiprodjo
authors.forename: Z
authors.initials: Z
authors.lastname: Deak
authors.forename: F
authors.initials: F
authors.lastname: Meurer
authors.forename: F
authors.initials: F
authors.lastname: Maertz
authors.forename: F G
authors.initials: FG
authors.lastname: Mueck
authors.forename: L L
authors.initials: LL
authors.lastname: Geyer
authors.forename: S
authors.initials: S
authors.lastname: Wirth
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D001927:Brain Diseases / Q000000981:diagnostic imaging*; D003287:Contrast Media; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011446:Prospective Studies; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods; D055815:Young Adult
chemical_list: D003287:Contrast Media
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