OBJECTIVE: The overall accuracy of neuronavigation systems may be influenced by (1) the technical accuracy, (2) the registration process, (3) voxel size and/or distortion of image data and (4) intraoperative events. The aim of this study was to test the influence of the registration and imaging modality on the accuracy. METHODS: A plexiglas phantom with 32 rods was taken for navigation targeting. Sixteen fiducials were attached to the surface of the phantom forming two different attachment patterns (clustered vs. diffusely scattered). This model was scanned by MRI and CT (1-mm slices). Registration was performed using different numbers and attachment patterns of the fiducials. Using CT or MRI, the localization error was measured in image space as the Euclidean distance between targets defined in image space and those detected in the physical space. Accuracy was measured with two commercial systems, the Zeiss MKM and the StealthStation. RESULTS: The mean localization error varied between 1.59 +/- 0.29 mm (MKM, 8 scattered fiducials, CT scanning) and 3.86 +/- 2.19 mm (MKM, 4 clustered fiducials, MRI). The worst localization error was 9.5 mm (MKM). In case of an optimal registration, the 95th percentile for the localization error was 2.2 (MKM) and 2.75 mm (StealthStation). The imaging modality has only minor influence on the localization error, with CT increasing accuracy minimally. Both the fiducial number and the attachment pattern critically influence the localization error: 8 fiducials and a generalized attachment pattern increase the accuracy significantly. No correlation between the calculated registration accuracy and the measured localization accuracy was found. CONCLUSION: The application accuracy of different neuronavigation systems critically depends on the registration. The calculated registration accuracy provided by the system does not correspond to the localization error found in reality. The accuracy of frameless neuronavigation systems is comparable to that of classical frame-based stereotactic devices. Copyright 2002 S. Karger AG, Basel
OBJECTIVE: The overall accuracy of neuronavigation systems may be influenced by (1) the technical accuracy, (2) the registration process, (3) voxel size and/or distortion of image data and (4) intraoperative events. The aim of this study was to test the influence of the registration and imaging modality on the accuracy. METHODS: A plexiglas phantom with 32 rods was taken for navigation targeting. Sixteen fiducials were attached to the surface of the phantom forming two different attachment patterns (clustered vs. diffusely scattered). This model was scanned by MRI and CT (1-mm slices). Registration was performed using different numbers and attachment patterns of the fiducials. Using CT or MRI, the localization error was measured in image space as the Euclidean distance between targets defined in image space and those detected in the physical space. Accuracy was measured with two commercial systems, the Zeiss MKM and the StealthStation. RESULTS: The mean localization error varied between 1.59 +/- 0.29 mm (MKM, 8 scattered fiducials, CT scanning) and 3.86 +/- 2.19 mm (MKM, 4 clustered fiducials, MRI). The worst localization error was 9.5 mm (MKM). In case of an optimal registration, the 95th percentile for the localization error was 2.2 (MKM) and 2.75 mm (StealthStation). The imaging modality has only minor influence on the localization error, with CT increasing accuracy minimally. Both the fiducial number and the attachment pattern critically influence the localization error: 8 fiducials and a generalized attachment pattern increase the accuracy significantly. No correlation between the calculated registration accuracy and the measured localization accuracy was found. CONCLUSION: The application accuracy of different neuronavigation systems critically depends on the registration. The calculated registration accuracy provided by the system does not correspond to the localization error found in reality. The accuracy of frameless neuronavigation systems is comparable to that of classical frame-based stereotactic devices. Copyright 2002 S. Karger AG, Basel
Authors: G Strauss; M Hofer; W Korb; C Trantakis; D Winkler; O Burgert; T Schulz; A Dietz; J Meixensberger; K Koulechov Journal: HNO Date: 2006-02 Impact factor: 1.284
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Authors: Vassilios I Vougioukas; Ulrich Hubbe; Albrecht Hochmuth; Nils C Gellrich; Vera van Velthoven Journal: Childs Nerv Syst Date: 2003-10-11 Impact factor: 1.475