More accurate Talairach coordinates for neuroimaging using non-linear registration.
While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.
Lacadie
Cheryl M
CM
Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT, USA.
Fulbright
Robert K
RK
Rajeevan
Nallakkandi
N
Constable
R Todd
RT
Papademetris
Xenophon
X
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique