title: Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography.
abstract: BACKGROUND
  We aimed to develop an automated algorithm (APQ) for accurate volumetric quantification of non-calcified (NCP) and calcified plaque (CP) from coronary CT angiography (CCTA).
  
  
  METHODS
  APQ determines scan-specific attenuation thresholds for lumen, NCP, CP and epicardial fat, and applies knowledge-based segmentation and modeling of coronary arteries, to define NCP and CP components in 3D. We tested APQ in 29 plaques for 24 consecutive scans, acquired with dual-source CT scanner. APQ results were compared to volumes obtained by manual slice-by-slice NCP/CP definition and by interactive adjustment of plaque thresholds (ITA) by 2 independent experts.
  
  
  RESULTS
  APQ analysis time was <2 sec per lesion. There was strong correlation between the 2 readers for manual quantification (r = 0.99, p < 0.0001 for NCP; r = 0.85, p < 0.0001 for CP). The mean HU determined by APQ was 419 +/- 78 for luminal contrast at mid-lesion, 227 +/- 40 for NCP upper threshold, and 511 +/- 80 for the CP lower threshold. APQ showed a significantly lower absolute difference (26.7 mm(3) vs. 42.1 mm(3), p = 0.01), lower bias than ITA (32.6 mm(3) vs 64.4 mm(3), p = 0.01) for NCP. There was strong correlation between APQ and readers (R = 0.94, p < 0.0001 for NCP volumes; R = 0.88, p < 0.0001, for CP volumes; R = 0.90, p < 0.0001 for NCP and CP composition).
  
  
  CONCLUSIONS
  We developed a fast automated algorithm for quantification of NCP and CP from CCTA, which is in close agreement with expert manual quantification.
authors.affiliation: Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Taper Building, A238, Los Angeles, CA 90048, USA. deyd@cshs.org
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.forename: Victor Y
authors.initials: VY
authors.lastname: Cheng
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.forename: Ryo
authors.initials: R
authors.lastname: Nakazato
authors.forename: Amit
authors.initials: A
authors.lastname: Ramesh
authors.forename: Swaminatha
authors.initials: S
authors.lastname: Gurudevan
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002114:Calcinosis / Q000000981:diagnostic imaging*; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index; D012984:Software; D014057:Tomography, X-Ray Computed