title: Carotid artery stenosis: performance of advanced vessel analysis software in evaluating CTA.
abstract: OBJECTIVE
  The aim of this study was to evaluate time efficiency and diagnostic reproducibility of an advanced vessel analysis software for diagnosis of carotid artery stenosis.
  
  
  METHODS
  40 patients with suspected carotid artery stenosis received head and neck DE-CTA as part of their pre-interventional workup. Acquired data were evaluated by 2 independent radiologists. Stenosis grading was performed by MPR eyeballing with freely adjustable MPRs and with a preliminary prototype of the meanwhile available client-server and advanced visualization software syngo.via CT Vascular (Siemens Healthcare, Erlangen, Germany). Stenoses were graded according to the following 5 categories: I: 0%, II: 1-50%, III: 51-69%, IV: 70-99% and V: total occlusion. Furthermore, time to diagnosis for each carotid artery was recorded.
  
  
  RESULTS
  Both readers achieved very good specificity values and good respectively very good sensitivity values without significant differences between both reading methods. Furthermore, there was a very good correlation between both readers for both reading methods without significant differences (kappa value: standard image interpretation k=0.809; advanced vessel analysis software k=0.863). Using advanced vessel analysis software resulted in a significant time saving (p<0.0001) for both readers. Time to diagnosis could be decreased by approximately 55%.
  
  
  CONCLUSIONS
  Advanced vessel analysis application CT Vascular of the new imaging software syngo.via (Siemens Healthcare, Forchheim, Germany) provides a high rate of reproducibility in assessment of carotid artery stenosis. Furthermore a significant time saving in comparison to standard image interpretation is achievable.
authors.affiliation: University Hospital of Tuebingen, Diagnostic and Interventional Radiology, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany. ilias.tsiflikas@med.uni-tuebingen.de
authors.forename: Ilias
authors.initials: I
authors.lastname: Tsiflikas
authors.forename: Christina
authors.initials: C
authors.lastname: Biermann
authors.forename: Christoph
authors.initials: C
authors.lastname: Thomas
authors.forename: Dominik
authors.initials: D
authors.lastname: Ketelsen
authors.forename: Claus D
authors.initials: CD
authors.lastname: Claussen
authors.forename: Martin
authors.initials: M
authors.lastname: Heuschmid
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D006801:Humans; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation; D014057:Tomography, X-Ray Computed / Q000379:methods*