title: High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction.
abstract: OBJECTIVE
  To compare the image quality of high-resolution computed tomography (HRCT) for evaluating lung nodules reconstructed with the new version of model-based iterative reconstruction and spatial resolution preference algorithm (MBIRn) vs. conventional model-based iterative reconstruction (MBIRc) and adaptive statistical iterative reconstruction (ASIR).
  
  
  METHODS
  This retrospective clinical study was approved by our institutional review board and included 70 lung nodules in 58 patients (mean age, 71.2±10.9years; 34 men and 24 women). HRCT of lung nodules were reconstructed using MBIRn, MBIRc and ASIR. Objective image noise was measured by placing the regions of interest on lung parenchyma. Two blinded radiologists performed subjective image analyses.
  
  
  RESULTS
  Significant improvements in the following points were observed in MBIRn compared with ASIR (p<0.005): objective image noise (24.4±8.0 vs. 37.7±10.4), subjective image noise, streak artifacts, and adequateness for evaluating internal characteristics and borders of nodules. The sharpness of small vessels and bronchi and diagnostic acceptability with MBIRn were significantly better than with MBIRc and ASIR (p<0.008).
  
  
  CONCLUSIONS
  HRCT reconstructed with MBIRn provides diagnostically more acceptable images for the detailed analyses of lung nodules compared with MBIRc and ASIR.
authors: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. Electronic address: koyasaka@gmail.com.
authors: Koichiro
authors: K
authors: Yasaka
authors: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
authors: Masaki
authors: M
authors: Katsura
authors: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
authors: Shouhei
authors: S
authors: Hanaoka
authors: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
authors: Jiro
authors: J
authors: Sato
authors: Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
authors: Kuni
authors: K
authors: Ohtomo
mesh_terms: D000368:Aged; D000465:Algorithms; D016477:Artifacts; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Image quality; Iterative reconstruction; Lung neoplasm; Multidetector computed tomography; Solitary pulmonary nodule