title: Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US.
abstract: OBJECTIVE
  To determine the accuracy of a previously developed automated algorithm (AUTOPLAQ [APQ]) for rapid volumetric quantification of noncalcified and calcified plaque from coronary computed tomographic (CT) angiography in comparison with intravascular ultrasonography (US).
  
  
  METHODS
  This study was approved by the institutional review board and was HIPAA compliant; all patients provided written informed consent. APQ combines derived scan-specific attenuation threshold levels for lumen, plaque, and knowledge-based segmentation of coronary arteries for quantification of plaque components. APQ was validated with retrospective analysis of 22 coronary atherosclerotic plaques in 20 patients imaged with coronary CT angiography and intravascular US within 2 days of each other. Coronary CT angiographic data were acquired by using dual-source CT. For each patient, well-defined plaques without calcifications were selected, and plaque volume was measured with APQ and manual tracing at CT and with intravascular US. Measurements were compared with paired t test, correlation, and Bland-Altman analysis.
  
  
  RESULTS
  There was excellent correlation between noncalcified plaque volumes quantified with APQ and intravascular US (r = 0.94, P < .001), with no significant differences (P = .08). Mean plaque volume with intravascular US was 105.9 mm³ ± 83.5 (standard deviation) and with APQ was 116.6 mm³ ± 80.1. Mean plaque volume with manual tracing from CT was 100.8 mm³ ± 81.7 and with APQ was 116.6 mm³ ± 80.1, with excellent correlation (r = 0.92, P < .001) and no significant differences (P = .23).
  
  
  CONCLUSIONS
  Automated scan-specific threshold level-based quantification of plaque components from coronary CT angiography allows rapid, accurate measurement of noncalcified plaque volumes, compared with intravascular US, and requires a fraction of the time needed for manual analysis.
authors.affiliation: Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, and David-Geffen School of Medicine, UCLA, 8700 Beverly Blvd, Taper Building A238, Los Angeles, CA 90048, USA. deyd@cshs.org
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.forename: Tiziano
authors.initials: T
authors.lastname: Schepis
authors.forename: Mohamed
authors.initials: M
authors.lastname: Marwan
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.forename: Stephan
authors.initials: S
authors.lastname: Achenbach
mesh_terms: D000465:Algorithms; D001331:Automation; D003287:Contrast Media; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D007479:Iopamidol / Q000031:analogs & derivatives; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D013997:Time Factors; D014057:Tomography, X-Ray Computed; D018084:Ultrasonography, Interventional / Q000379:methods*
chemical_list: D003287:Contrast Media; C057937:iomeprol; D007479:Iopamidol