Enantiomorphic normalization of focally lesioned brains.
In order to make spatial inferences about the brain across a group of patients, it is usually necessary to employ some means of bringing each brain image into register with either a group mean image or a standard template. In the presence of focal brain lesions, automated methods for performing such so-called normalization are liable to distortion from the abnormal signal within the lesion, especially when the non-linear warping necessary for maximum registration fidelity is used. The most frequently used method for minimizing this distortion--cost function masking--simply eliminates the lesioned area when deriving the normalization parameters. As lesion size increases, however, the normalization error may be expected to rise steeply since the volume of brain from which the parameters are derived falls with it. Here we propose an alternative non-linear registration method that exploits a natural redundancy in the brain--the enantiomorphic relation between the two hemispheres--to correct the signal within the lesion using information from the undamaged homologous region within the contralesional hemisphere. As lesion size increases, the normalization error should theoretically asymptote to inter-hemispheric differences, which are both quantifiable and much lower than the inter-subject difference. Using SPM's non-linear normalization routines, we evaluate this technique with images of normal brains to which lesions selected from a large dataset have been artificially applied. Our results show the enantiomorphic method to be vastly superior to cost function masking across subjects, lesion characteristics, and brain voxels. We therefore propose that it should be the method of choice for normalizing images of focally lesioned brains.
Nachev
Parashkev
P
Department of Clinical Neuroscience, Imperial College London, UK. p.nachev@imperial.ac.uk
Coulthard
Elizabeth
E
Jäger
H Rolf
HR
Kennard
Christopher
C
Husain
Masud
M
D000465:Algorithms; D001284:Atrophy; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D017711:Nonlinear Dynamics; D012016:Reference Values; D013238:Stereotaxic Techniques; D020521:Stroke / Q000473:pathology