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. MATERIALS AND 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. CONCLUSION: 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.
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. MATERIALS AND 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. CONCLUSION: 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.
Authors: Ashley M Groves; Vicky Goh; Sabarinath Rajasekharan; Irfan Kayani; Raymondo Endozo; John C Dickson; Leon J Menezes; Manu Shastry; Said B Habib; Peter J Ell; Brian F Hutton Journal: Eur Radiol Date: 2008-05-09 Impact factor: 5.315
Authors: P C Sanelli; G Nicola; R Johnson; A J Tsiouris; I Ougorets; C Knight; B Frommer; S Veronelli; R D Zimmerman Journal: AJNR Am J Neuroradiol Date: 2007-03 Impact factor: 3.825
Authors: Bruno P Soares; Jan Willem Dankbaar; Joerg Bredno; SuChun Cheng; Sumail Bhogal; William P Dillon; Max Wintermark Journal: Neuroradiology Date: 2009-03-10 Impact factor: 2.804
Authors: Anna Maria Di Nallo; Antonello Vidiri; Simona Marzi; Alessandra Mirri; Alessandra Fabi; Carmine Maria Carapella; Andrea Pace; Marcello Crecco Journal: J Exp Clin Cancer Res Date: 2009-03-16