title: Fully automatic model-based calcium segmentation and scoring in coronary CT angiography.
abstract: OBJECTIVE
  The paper presents new methods for automatic coronary calcium detection, segmentation and scoring in coronary CT angiography (cCTA) studies.
  
  
  METHODS
  Calcium detection and segmentation are performed by modeling image intensity profiles of coronary arteries. The scoring algorithm is based on a simulated unenhanced calcium score (CS) CT image, constructed by virtually removing the contrast media from cCTA. The methods are implemented as part of a fully automatic system for CS assessment from cCTA.
  
  
  RESULTS
  The system was tested in two independent clinical trials on 263 studies and demonstrated 0.95/0.91 correlation between the CS computed from cCTA and the standard Agatston score derived from unenhanced CS CT. The mean absolute percent difference (MAPD) of 36/39 % between the two scores lies within the error range of the standard CS CT (15-65 %).
  
  
  CONCLUSIONS
  High diagnostic performance, combined with the benefits of the fully automatic solution, suggests that the proposed technique can be used to eliminate the need in a separate CS CT scan as part of the cCTA examination, thus reducing the radiation exposure and simplifying the procedure.
authors.affiliation: Rcadia Medical Imaging, 157 Yafo Str., 35251 , Haifa, Israel, dov.eilot@gmail.com.
authors.forename: Dov
authors.initials: D
authors.lastname: Eilot
authors.forename: Roman
authors.initials: R
authors.lastname: Goldenberg
mesh_terms: D000465:Algorithms; D002114:Calcinosis / Q000000981:diagnostic imaging*; D002118:Calcium / Q000032:analysis*; D003287:Contrast Media; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male
chemical_list: D003287:Contrast Media; D002118:Calcium