title: Validation of a method to differentiate arterial and venous vessels in CT perfusion data using linear combinations of quantitative time-density curve characteristics.
abstract: OBJECTIVE
  We aimed to develop and evaluate a new method that reliably differentiates between cerebral arteries and veins using voxel-wise CT-perfusion-derived parameters.
  
  
  METHODS
  Fourteen consecutive patients with suspected stroke but without pathological findings were examined on a multi-detector CT system: 32 dynamic phases (∆t = 1.5 s) during application of 35 mL iomeprol-350 were acquired at 80 kV/200mAs. Three hemodynamic parameters were calculated for 18 arterial and venous vessel segments: A (maximum of the time-density-curve), T (time-to-peak), and W (full-width-at-half-maximum). Using receiver operator characteristic (ROC) curve analysis and Fisher's linear discriminant analysis (FLDA), the performance of every classifier (A, T, W) and of all linear combinations for the differentiation of arterial and venous vessels was determined.
  
  
  RESULTS
  A maximum area under the ROC-curve (AUC) of 0.945 (accuracy = 86.8%) was obtained using the FLDA combination of A&T or the triplet FLDA of A&T&W for the classification of venous and arterial vessels. The best single parameter was T with an AUC of 0.871 (accuracy = 79.0%), which performed significantly worse than the combination A&T (p < 0.001).
  
  
  CONCLUSIONS
  Arteries and veins can be accurately differentiated based on dynamic CT perfusion data using the maximum of the time-density curve, its time-to-peak, its width, and FLDA combinations of these parameters, which yield accuracies up to 87%.
  
  
  CONCLUSIONS
  • For classification of cerebral vasculature, time-to-peak has the best single-parameter accuracy. • Fisher's linear discriminant analysis improves the performance of the individual classifiers. • Combining signal maximum and time-to-peak parameters significantly increased the classifying potential. • Pre-processing of time-density-curves by Gaussian filtering or fitting can improve diagnostic accuracy.
authors.affiliation: Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Marchioninistr. 15, 81377, Munich, Germany, lukas.havla@med.uni-muenchen.de.
authors.forename: Lukas
authors.initials: L
authors.lastname: Havla
authors.forename: Moritz
authors.initials: M
authors.lastname: Schneider
authors.forename: Kolja M
authors.initials: KM
authors.lastname: Thierfelder
authors.forename: Sebastian E
authors.initials: SE
authors.lastname: Beyer
authors.forename: Birgit
authors.initials: B
authors.lastname: Ertl-Wagner
authors.forename: Wieland H
authors.initials: WH
authors.lastname: Sommer
authors.forename: Olaf
authors.initials: O
authors.lastname: Dietrich
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002536:Cerebral Arteries / Q000000981:diagnostic imaging* / Q000502:physiology; D002550:Cerebral Veins / Q000000981:diagnostic imaging* / Q000502:physiology; D003287:Contrast Media; D005260:Female; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D007479:Iopamidol / Q000031:analogs & derivatives; D008297:Male; D008875:Middle Aged; D010690:Phlebography / Q000379:methods; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult
chemical_list: D003287:Contrast Media; C057937:iomeprol; D007479:Iopamidol
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