title: Effect of radiologists' experience with an adaptive statistical iterative reconstruction algorithm on detection of hypervascular liver lesions and perception of image quality.
abstract: OBJECTIVE
  To prospectively evaluate whether clinical experience with an adaptive statistical iterative reconstruction algorithm (ASiR) has an effect on radiologists' diagnostic performance and confidence for the diagnosis of hypervascular liver tumors, as well as on their subjective perception of image quality.
  
  
  METHODS
  Forty patients, having 65 hypervascular liver tumors, underwent contrast-enhanced MDCT during the hepatic arterial phase. Image datasets were reconstructed with filtered backprojection algorithm and ASiR (20%, 40%, 60%, and 80% blending). During two reading sessions, performed before and after a three-year period of clinical experience with ASiR, three readers assessed datasets for lesion detection, likelihood of malignancy, and image quality.
  
  
  RESULTS
  For all reconstruction algorithms, there was no significant change in readers' diagnostic accuracy and sensitivity for the detection of liver lesions, between the two reading sessions. However, a 60% ASiR dataset yielded a significant improvement in specificity, lesion conspicuity, and confidence for lesion likelihood of malignancy during the second reading session (P < 0.0001). The 60% ASiR dataset resulted in significant improvement in readers' perception of image quality during the second reading session (P < 0.0001).
  
  
  CONCLUSIONS
  Clinical experience using an ASiR algorithm may improve radiologists' diagnostic performance for the diagnosis of hypervascular liver tumors, as well as their perception of image quality.
authors.affiliation: Department of Radiology, Duke University Medical Center, Erwin Road, Durham, NC, 27710, USA. daniele.marin@duke.edu.
authors.forename: Daniele
authors.initials: D
authors.lastname: Marin
authors.affiliation: Department of Radiology, Duke University Medical Center, Erwin Road, Durham, NC, 27710, USA.
authors.forename: Achille
authors.initials: A
authors.lastname: Mileto
authors.affiliation: Department of Radiology, Duke University Medical Center, Erwin Road, Durham, NC, 27710, USA.
authors.forename: Rajan T
authors.initials: RT
authors.lastname: Gupta
authors.affiliation: Department of Radiology, Duke University Medical Center, Erwin Road, Durham, NC, 27710, USA.
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Ho
authors.affiliation: Department of Radiology, Wake Forest Baptist Medical Center, Winston-Salem, NC, 27157, USA.
authors.forename: Brian C
authors.initials: BC
authors.lastname: Allen
authors.affiliation: Carl E. Ravin Advanced Imaging Laboratories (RAI Labs), Duke University Medical Center, Durham, NC, 27710, USA.
authors.forename: Kingshuk Roy
authors.initials: KR
authors.lastname: Choudhury
authors.affiliation: Department of Radiology, Duke University Medical Center, Erwin Road, Durham, NC, 27710, USA.
authors.forename: Rendon C
authors.initials: RC
authors.lastname: Nelson
mesh_terms: D000368:Aged; D000465:Algorithms; D002983:Clinical Competence; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted
chemical_list: D003287:Contrast Media
keywords: Adaptive statistical iterative reconstruction; Diagnostic accuracy; Filtered backprojection; Hypervascular liver tumors; Image quality