Reproducibility of postprocessing of quantitative CT perfusion maps.
OBJECTIVE
The purpose of this study was to assess interobserver and intraobserver variability in evaluation of the reproducibility of quantitative data obtained in semiautomated postprocessing of CT perfusion data sets by observers of different levels of skill and experience and in fully automated postprocessing.


METHODS
Twenty CT perfusion data sets were postprocessed by a neuroradiologist using an automated postprocessing program and by five observers (neuroradiology attending, neurology attending, radiology resident, senior and junior CT technologists) who received a brief training session in use of software for semiautomated postprocessing. For assessment of intraobserver variability, each observer repeated postprocessing of 10 CT perfusion data sets. Standard regions of interest were placed on identical locations for each observer's cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) maps of three brain regions: an ischemia-infarct region, normal cortical gray matter, and white matter.


RESULTS
The variability in mean quantitative values of CBF, CBV, and MTT was 2.5-9.5% among all observers. Greater variability (20.4%) was introduced with the automated program. High correlation was found among all possible pairings of observers (r = 0.87-0.99). Low correlation was observed between automated postprocessing and postprocessing by all observers. Intraobserver variability in quantitative CT perfusion data ranged from 0.29% to 10.8%. High intraobserver correlation (r = 0.91-0.99) was found for the observers.


CONCLUSIONS
Quantitative CBF, CBV, and MTT data obtained from postprocessing of CT perfusion data sets are reproducible among observers with varying levels of skill and experience. Observer interaction with the software is an important component for correct identification of user-defined parameters. Establishing a uniform and standard postprocessing technique is essential for maintaining good reproducibility.
Sanelli
Pina C
PC
Department of Radiology, New York Presbyterian Hospital, Weill Medical College of Cornell University, 520 E 70th St., Starr 630, New York, NY 10021, USA. pcs9001@med.cornell.edu
Nicola
Gregory
G
Tsiouris
Apostolos J
AJ
Ougorets
Igor
I
Knight
Charles
C
Frommer
Bruce
B
Veronelli
Steve
S
Zimmerman
Robert D
RD
D000465:Algorithms; D001783:Blood Flow Velocity; D001921:Brain / Q000098:blood supply* / Q000000981:diagnostic imaging*; D001927:Brain Diseases / Q000000981:diagnostic imaging*; D002560:Cerebrovascular Circulation; D006801:Humans; 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*