title: Comparison of iterative model-based reconstruction versus conventional filtered back projection and hybrid iterative reconstruction techniques: lesion conspicuity and influence of body size in anthropomorphic liver phantoms.
abstract: OBJECTIVE
  This study aimed to determine whether an iterative model-based reconstruction (IMR) can improve lesion conspicuity and depiction on computed tomography (CT) compared with filtered back projection (FBP) and hybrid iterative reconstruction (iDose) using anthropomorphic phantoms.
  
  
  METHODS
  One small and one large anthropomorphic body phantoms, each containing 8 simulated focal liver lesions (FLLs), were scanned using a 256-channel CT scanner at 120 kVp with variable tube current-time products (10-200 mAs). Scans were divided into 3 groups based on radiation dose (RD) as follows: (a) full dose (FD), (b) low dose (FD50), and (c) ultralow dose (FD25 for the large phantom, FD15 for the small phantom). All images were reconstructed using FBP, iDose, and IMR. Image noise and lesion-to-liver contrast were assessed quantitatively and qualitatively. Thereafter, 6 radiologists independently evaluated conspicuity of FLLs, and then, compared the number of invisible FLLs on 3 image sets of each RD group.
  
  
  RESULTS
  Image noise was significantly lower with IMR than with FBP and iDose at the same RD. Iterative model-based reconstruction improved conspicuity of low-contrast FLLs in all RD groups compared to the others (P < 0.001). Furthermore, compared to FBP and iDose, the number of visible FLLs significantly increased on IMR images in the FD15 group of the small phantom 52.8% [38/72], 68.1% [49/72], and 84.8% [61/72], respectively; P < 0.001) and in the FD 25, FD50 groups of the large phantom (FD50: 56.9% [41/72], 76.4% [55/72], and 84.7% [61/72], respectively; P < 0.05).
  
  
  CONCLUSIONS
  Iterative model-based reconstruction reduced image noise and improved low-contrast FLL conspicuity, compared to FBP and iDose. Therefore, depiction of low-contrast FLLs on FBP could be improved using IMR.
authors.affiliation: From the *Department of Radiology, Seoul National University Hospital; †Institute of Radiation Medicine, Seoul National University College of Medicine; ‡Department of Radiology, Konkuk University Hospital; §Human Medical Imaging and Intervention Center; ∥Department of Radiology, Seoul National University Boramae Hospital, Seoul, Korea; ¶Philips Healthcare, Cleveland, OH; and #Department of Radiology, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea.
authors.forename: Jeong Hee
authors.initials: JH
authors.lastname: Yoon
authors.forename: Jeong Min
authors.initials: JM
authors.lastname: Lee
authors.forename: Mi Hye
authors.initials: MH
authors.lastname: Yu
authors.forename: Jee Hyun
authors.initials: JH
authors.lastname: Baek
authors.forename: Ju Hyun
authors.initials: JH
authors.lastname: Jeon
authors.forename: Bo Yun
authors.initials: BY
authors.lastname: Hur
authors.forename: Amar
authors.initials: A
authors.lastname: Dhanantwari
authors.forename: Se Young
authors.initials: SY
authors.lastname: Chung
authors.forename: Joon Koo
authors.initials: JK
authors.lastname: Han
authors.forename: Byung Ihn
authors.initials: BI
authors.lastname: Choi
mesh_terms: D049628:Body Size; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008953:Models, Anatomic; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*