Towards a validation of atlas warping techniques.
Pre-operative magnetic resonance (MRI) and computed tomography (CT) image volumes are often used for planning and guidance during functional neurosurgical procedures. These operations can include the creation of lesions in the thalamus (thalamotomy) or the globus pallidus (pallidotomy), or the insertion of deep brain stimulation (DBS) electrodes in the subcortical nuclei. These subcortical targets are often difficult to localize in pre-operative imaging data due to the limited resolution and contrast available in standard MRI or CT techniques. To address this problem, digital atlases of subcortical nuclei are often used to accurately identify surgical targets since they can be warped to fit each patient's unique anatomy. Targeting accuracy thus depends on the quality of the atlas-to-patient warp. In this paper, three atlas-to-patient warping techniques are compared. Two methods rely on an MRI template as an intermediary to estimate a nonlinear atlas-to-patient transformation. The third is novel, and uses a pseudo-MRI derived from an atlas of the basal ganglia and thalamus to estimate the nonlinear atlas-to-patient transformation directly. The methods are compared using (1) manual segmentations of subcortical nuclei and (2) functional data from intra-operative thalamic stimulation. The results demonstrate that the template-based atlas-to-patient warping technique is the best of the three for customizing the atlas onto patient data.
Chakravarty
M Mallar
MM
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada. mallar@bic.mni.mcgill.ca
Sadikot
Abbas F
AF
Germann
Jürgen
J
Bertrand
Gilles
G
Collins
D Louis
DL
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; 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*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique