title: Assessment of intracranial arterial stenosis with multidetector row CT angiography: a postprocessing techniques comparison.
abstract: OBJECTIVE
  It was demonstrated the some patients with stroke have intracranial stenosis of 50% or greater and the identification of intracranial arterial stenosis is extremely important in order to plan a correct therapeutical approach. The aim of this study was to assess the image quality and intertechnique agreement of various postprocessing methods in the detection of intracranial arterial stenosis.
  
  
  METHODS
  Eighty-five patients who were studied by using a multidetector row CT scanner were retrospectively analyzed. A total of 2040 segments were examined in the 85 subjects. Intracranial vasculature was assessed by using MPR, CPR, MIP, and VR techniques. Two radiologists reviewed the CT images independently. Cohen weighted kappa statistic was applied to calculate interobserver agreement and for image accuracy for each reconstruction method. Sensitivity, specificity, PPV, and NPV were also calculated by using the consensus read as the reference.
  
  
  RESULTS
  Two hundred fifteen (10.5%) stenosed artery segments were identified by the observers in consensus. The best intermethod kappa values between observers 1 and 2 were obtained by VR and MIP (kappa values of 0.878 and 0.861, respectively), whereas MPR provided the lowest value (kappa value of 0.282). VR showed a sensitivity for detecting stenosed segments of 88.8% and 91.6% for observers 1 and 2, respectively. The highest positive predictive value was also obtained by VR at 95% and 99% for observers 1 and 2, respectively. Image accuracy obtained by using VR was the highest among all reconstruction methods in both observers (185/255 and 177/255 for observers 1 and 2, respectively).
  
  
  CONCLUSIONS
  The results of our study suggest that VR and MIP techniques provide the best interobserver and intertechnique concordance in the analysis of intravascular cranial stenosis.
authors.affiliation: Department of Radiology, Policlinico Universitario, University of Cagliari, Monserrato, Cagliari, Italy. lucasaba@tiscali.it
authors.forename: L
authors.initials: L
authors.lastname: Saba
authors.forename: R
authors.initials: R
authors.lastname: Sanfilippo
authors.forename: R
authors.initials: R
authors.lastname: Montisci
authors.forename: G
authors.initials: G
authors.lastname: Mallarini
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002533:Cerebral Angiography / Q000379:methods*; D003251:Constriction, Pathologic / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D020765:Intracranial Arterial Diseases / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*