Normalization of brain images is a crucial step in MRI data analysis, especially when dealing with abnormal brains. Although cost function masking (CFM) appears to successfully solve this problem and seems to be necessary for patients with chronic stroke lesions, this procedure is very time consuming. The present study sought to find viable, fully automated alternatives to cost function masking, such as Automatic Lesion Identification (ALI) and Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL). It also sought to quantitatively assess, for the first time, Symmetrical Normalization (SyN) with constrained cost function masking. The second aim of this study was to investigate the normalization process in a group of drug-resistant epileptic patients with large resected regions (temporal lobe and amygdala) and in a group of stroke patients. A dataset of 500 artificially generated lesions was created using ten patients with brain-resected regions (temporal lobectomy), ten stroke patients and twenty five-healthy subjects. The results indicated that although a fully automated method such as DARTEL using New Segment with an extra prior (the mean of the white matter and cerebro-spinal fluid) obtained the most accurate normalization in both patient groups, it produced a shrinkage in lesion volume when compared to Unified Segmentation with CFM. Taken together, these findings suggest that further research is needed in order to improve automatic normalization processes in brains with large lesions and to completely abandon manual, time consuming normalization methods.
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