Abstract
Object: The aim of this study was to investigate whether diffusion tensor imaging (DTI) can be integrated into functional navigation for the intraoperative visualization of the pyramidal tract. Methods: A single-shot spin-echo diffusion-weighted echo planar imaging sequence on a 1.5 T magnetic resonance (MR) scanner was used for DTI. One null image and six diffusion-weighted images (high B value 1 000 mm/s2 ) were obtained. Color-encoded fractional anisotropy maps of the principal eigenvector rendered as a boxoid within each voxel were used for segmentation of the pyramidal tract. The segmented images were rigidly registered with a T1 -weighted gradient echo 3D dataset for navigation in 16 patients with gliomas. In tumors adjacent to the motor cortex (n = 6) data from functional MR imaging were co-registered. Results: The whole DTI processing lasted about 25-30 minutes in each case. In all cases DTI could be integrated into the navigational dataset resulting in an intraoperative visualization of the pyramidal tract by microscope-based navigation. Navigational accuracy measured as the target registration error was 1.2 ± 0.46 mm. Registration of fractional anisotropy maps with the 3D navigational dataset was possible with an error of less than 2 mm. Co-registration with fMRI was consistent with DTI data. A neurological deterioration was observed only in one patient. Conclusions: DTI can be reliably integrated into navigational datasets. Thus, microscope-based neuronavigation can be used for an intraoperative visualization of the course of the pyramidal tract. However, a possible shifting of the pyramidal tract has to be taken into account after major tumor parts are removed.
Zusammenfassung
Einleitung: Ziel war es, zu untersuchen, ob die Diffusions-Tensor-Bildgebung (DTI) in die funktionelle Neuronavigation zur intraoperativen Darstellung der Pyramidenbahn eingebunden werden kann. Material und Methoden: Die DTI-Daten wurden mit einer single-shot diffusions-gewichteten echo-planar-imaging-Sequenz an einem 1,5 Tesla Magnetresonanz-Scanner gemessen. Je Schicht wurden eine Messung ohne Diffusionsgradienten (B0 -Bild) sowie Messungen mit Diffusionsgradienten (b-Wert 1 000 s/mm2 ) in 6 verschiedenen Richtungen akquiriert. In farbkodierten Schichten der fraktionellen Anisotropie (FA), in denen in jedem Voxel der Hauteigenvektor als kleiner Quader dargestellt ist, wurde die Pyramidenbahn segmentiert. Bei insgesamt 16 Patienten mit Gliomen wurden diese segmentierten Bilddaten dann starr mit einem 3D T1 -gewichteten Navigationsdatensatz registriert. Bei Tumoren in der Nähe des motorischen Kortex (n = 6) wurden zusätzlich funktionelle MR-Messungen ko-registriert. Ergebnisse: Die gesamte Verarbeitung der DTI-Daten dauerte bei jedem Patienten zwischen 25 und 30 Minuten. In allen Fällen konnten die DTI-Daten in die Navigation integriert und so konnte die Pyramidenbahn mit Hilfe der mikroskop-gestützten Navigation während der Operation dargestellt werden. Die Navigationsgenauigkeit, gemessen als „Target Registration Error” betrug 1,2 ± 0,46 mm. Der Fehler der Registrierung der FA-Schichten mit den anatomischen Bilddaten war kleiner als 2 mm. Die Ko-Registrierung mit funktionellem MR war mit den DTI-Messungen konsistent. Bei nur einem Patienten kam es zu einer neurologischen Verschlechterung. Schlussfolgerung: DTI-Daten können zuverlässig in die Navigation eingebunden werden. Damit kann die mikroskop-gestützte Navigation zur intraoperativen Visualisierung der Pyramidenbahn eingesetzt werden. Jedoch muss eine mögliche Verlagerung der Pyramidenbahn, nachdem wesentliche Tumoranteile entfernt worden sind, berücksichtigt werden.
Key words
diffusion tensor imaging - functional neuronavigation - functional magnetic resonance imaging - white matter tracts
Schlüsselwörter
Bahnsysteme - Diffusions-Tensor-Bildgebung - funktionelle Neuronavigation - funktionelle Magnetresonanztomographie
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C. NimskyMD
Department of Neurosurgery · University Erlangen-Nürnberg
Schwabachanlage 6
91054 Erlangen
Germany
Phone: +49/9131/8 53 45 70
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Email: nimsky@nch.imed.uni-erlangen.de