title: Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.
abstract: OBJECTIVE
  The purpose of this work was to evaluate the image quality, lesion conspicuity, and dose reduction provided by knowledge-based iterative model reconstruction (IMR) in computed tomography (CT) of the liver compared with hybrid iterative reconstruction (IR) and filtered back projection (FBP) in patients with hepatocellular carcinoma (HCC).
  
  
  METHODS
  Fifty-six patients with 61 HCCs who underwent multiphasic reduced-dose CT (RDCT; n = 33) or standard-dose CT (SDCT; n = 28) were retrospectively evaluated. Reconstructed images with FBP, hybrid IR (iDose), IMR were evaluated for image quality using CT attenuation and image noise. Objective and subjective image quality of RDCT and SDCT sets were independently assessed by 2 observers in a blinded manner.
  
  
  RESULTS
  Image quality and lesion conspicuity were better with IMR for both RDCT and SDCT than either FBP or IR (P < 0.001). Contrast-to-noise ratio of HCCs in IMR-RDCT was significantly higher on delayed phase (DP) (P < 0.001), and comparable on arterial phase, than with IR-SDCT (P = 0.501). Iterative model reconstruction RDCT was significantly superior to FBP-SDCT (P < 0.001). Compared with IR-SDCT, IMR-RDCT was comparable in image sharpness and tumor conspicuity on arterial phase, and superior in image quality, noise, and lesion conspicuity on DP. With the use of IMR, a 27% reduction of effective dose was achieved with RDCT (12.7 ± 0.6 mSv) compared with SDCT (17.4 ± 1.1 mSv) without loss of image quality (P < 0.001).
  
  
  CONCLUSIONS
  Iterative model reconstruction provides better image quality and tumor conspicuity than FBP and IR with considerable noise reduction. In addition, more than comparable results were achieved with IMR-RDCT to IR-SDCT for the evaluation of HCCs.
authors.affiliation: From the *Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea, †Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea; and ‡Department of Radiology, Ulsan University Hospital, Ulsan University College of Medicine, Ulsan, Republic of Korea.
authors.forename: Hyun Jeong
authors.initials: HJ
authors.lastname: Park
authors.forename: Jeong Min
authors.initials: JM
authors.lastname: Lee
authors.forename: Sung Bin
authors.initials: SB
authors.lastname: Park
authors.forename: Jong Beum
authors.initials: JB
authors.lastname: Lee
authors.forename: Yoong Ki
authors.initials: YK
authors.lastname: Jeong
authors.forename: Jeong Hee
authors.initials: JH
authors.lastname: Yoon
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D051188:Knowledge Bases; D008099:Liver / Q000000981:diagnostic imaging; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D061330:Multidetector Computed Tomography / Q000379:methods*; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D056910:Republic of Korea; D012680:Sensitivity and Specificity