title: Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique.
abstract: OBJECTIVE
  To prospectively evaluate dose reduction and image quality characteristics of chest CT reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR).
  
  
  METHODS
  One hundred patients underwent reference-dose and low-dose unenhanced chest CT with 64-row multidetector CT. Images were reconstructed with 50 % ASIR-filtered back projection blending (ASIR50) for reference-dose CT, and with ASIR50 and MBIR for low-dose CT. Two radiologists assessed the images in a blinded manner for subjective image noise, artefacts and diagnostic acceptability. Objective image noise was measured in the lung parenchyma. Data were analysed using the sign test and pair-wise Student's t-test.
  
  
  RESULTS
  Compared with reference-dose CT, there was a 79.0 % decrease in dose-length product with low-dose CT. Low-dose MBIR images had significantly lower objective image noise (16.93 ± 3.00) than low-dose ASIR (49.24 ± 9.11, P < 0.01) and reference-dose ASIR images (24.93 ± 4.65, P < 0.01). Low-dose MBIR images were all diagnostically acceptable. Unique features of low-dose MBIR images included motion artefacts and pixellated blotchy appearances, which did not adversely affect diagnostic acceptability.
  
  
  CONCLUSIONS
  Diagnostically acceptable chest CT images acquired with nearly 80 % less radiation can be obtained using MBIR. MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT images without severely compromising image quality.
  
  
  CONCLUSIONS
  • Model-based iterative reconstruction (MBIR) creates high-quality low-dose CT images. • MBIR significantly improves image noise and artefacts over adaptive statistical iterative techniques. • MBIR shows greater potential than ASIR for diagnostically acceptable low-dose CT. • The prolonged processing time of MBIR may currently limit its routine use in clinical practice.
authors.affiliation: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. mkatsura-tky@umin.ac.jp
authors.forename: Masaki
authors.initials: M
authors.lastname: Katsura
authors.forename: Izuru
authors.initials: I
authors.lastname: Matsuda
authors.forename: Masaaki
authors.initials: M
authors.lastname: Akahane
authors.forename: Jiro
authors.initials: J
authors.lastname: Sato
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Akai
authors.forename: Koichiro
authors.initials: K
authors.lastname: Yasaka
authors.forename: Akira
authors.initials: A
authors.lastname: Kunimatsu
authors.forename: Kuni
authors.initials: K
authors.lastname: Ohtomo
mesh_terms: D000368:Aged; D000465:Algorithms; D016477:Artifacts; D001835:Body Weight; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009038:Motion; D019047:Phantoms, Imaging; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*