title: Multislice CT coronary angiography: evaluation of an automatic vessel detection tool.
abstract: OBJECTIVE
  To investigate the potential of a new detection tool for multislice CT (MSCT) coronary angiography with automatic display of curved multiplanar reformations and orthogonal cross-sections.
  
  
  METHODS
  Thirty-five patients were consecutively enrolled in a prospective intention-to-diagnose study and examined using a MSCT scanner with 16 x 0.5 mm detector collimation and 400 ms gantry rotation time (Aquilion, Toshiba). A multisegment algorithm using up to four segments was applied for ECG-gated reconstruction. Automatic and manual detection of coronary arteries was conducted using the coronary artery CT protocol of a workstation (Vitrea 2, Version 3.3, Vital Images) to detect significant stenoses (> or = 50 %) in all segments of > or = 1.5 mm in diameter. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of conventional coronary angiography.
  
  
  RESULTS
  The overall sensitivity, specificity, nondiagnostic rate, and accuracy of the automatic and manual approach were 90 vs. 94 %, 89 vs. 84 %, 6 vs. 6 %, and 89 vs. 88 %, respectively (p = n. s.). The vessel length detected with the automatic and manual approach were highly correlated for the left main/left anterior descending (143 +/- 30 vs. 146 +/- 24 mm, r = 0.923, p < 0.001), left circumflex (94 +/- 35 vs. 93 +/- 33 mm, r = 0.945, p < 0.001), and right coronary artery (145 +/- 36 vs. 144 +/- 37 mm, r = 0.925, p < 0.001). The time required to create reformations along the coronary arteries was significantly shorter with the automatic tool compared to the manual approach (203 +/- 77 vs. 391 +/- 104 sec, p < 0.005). In 90 % of the coronary branches automatic detection required less time than the manual approach.
  
  
  CONCLUSIONS
  Automatic coronary vessel detection is feasible and reduces the time required to create reformations by a factor of approximately two without deteriorating the diagnostic accuracy.
authors.affiliation: Department of Radiology, Charité, Medical School of the Free University and Humboldt University, Berlin. marc.dewey@charite.de
authors.forename: M
authors.initials: M
authors.lastname: Dewey
authors.forename: D
authors.initials: D
authors.lastname: Schnapauff
authors.forename: M
authors.initials: M
authors.lastname: Laule
authors.forename: A
authors.initials: A
authors.lastname: Lembcke
authors.forename: A C
authors.initials: AC
authors.lastname: Borges
authors.forename: W
authors.initials: W
authors.lastname: Rutsch
authors.forename: B
authors.initials: B
authors.lastname: Hamm
authors.forename: P
authors.initials: P
authors.lastname: Rogalla
mesh_terms: D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D012984:Software; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods*