title: Submillisievert CT using model-based iterative reconstruction with lung-specific setting: An initial phantom study.
abstract: OBJECTIVE
  To assess image quality of filtered back-projection (FBP) and model-based iterative reconstruction (MBIR) with a conventional setting and a new lung-specific setting on submillisievert CT.
  
  
  METHODS
  A lung phantom with artificial nodules was scanned with 10 mA at 120 kVp and 80 kVp (0.14 mSv and 0.05 mSv, respectively); images were reconstructed using FBP and MBIR with conventional setting (MBIRStnd) and lung-specific settings (MBIRRP20/Txand MBIRRP20). Three observers subjectively scored overall image quality and image findings on a 5-point scale (1 = worst, 5 = best) compared with reference standard images (50 mA-FBP at 120, 100, 80 kVp). Image noise was measured objectively.
  
  
  RESULTS
  MBIRRP20/Txperformed significantly better than MBIRStndfor overall image quality in 80-kVp images (p < 0.01), blurring of the border between lung and chest wall in 120p-kVp images (p < 0.05) and the ventral area of 80-kVp images (p < 0.001), and clarity of small vessels in the ventral area of 80-kVp images (p = 0.037). At 120 kVp, 10 mA-MBIRRP20and 10 mA-MBIRRP20/Txshowed similar performance to 50 mA-FBP. MBIRStndwas better for noise reduction. Except for blurring in 120 kVp-MBIRStnd, MBIRs performed better than FBP.
  
  
  CONCLUSIONS
  Although a conventional setting was advantageous in noise reduction, a lung-specific setting can provide more appropriate image quality, even on submillisievert CT.
  
  
  CONCLUSIONS
  • Lung-specific submillisievert 10 mA-MBIR CT setting has similar performance to 50 mA-FBP • The new lung-specific settings improve vessel clarity and blurring of borders • The new settings may provide more appropriate images than conventional settings.
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan. a-hata@radiol.med.osaka-u.ac.jp.
authors.forename: Akinori
authors.identifier: http://orcid.org/0000-0003-1149-7094
authors.initials: A
authors.lastname: Hata
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Yanagawa
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Osamu
authors.initials: O
authors.lastname: Honda
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Tomoko
authors.initials: T
authors.lastname: Gyobu
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Ken
authors.initials: K
authors.lastname: Ueda
authors.affiliation: Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Noriyuki
authors.initials: N
authors.lastname: Tomiyama
mesh_terms: D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Image enhancement; Image processing; Lung; Multidetector computed tomography; Radiation dosage