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Abstract: Curvilinear reformatting of three-dimensional (3D) MRI data of the cerebral cortex is a well-established tool which improves the display of the gyral structure, permits a precise localization of lesions, and helps to identify subtle abnormalities difficult to detect in planar slices due to the brain''s complex convolutional pattern. However, the method is time consuming because it requires interactive manual delineation of the brain surface contour. Therefore, a novel technique for automatic curvilinear reformatting is presented. A T1-weighted MRI volume data set is normalized using SPM2. Due to the normalization to a common stereotactic space, predefined masks can be applied to cover skull and outer brain regions in different depths from the brain surface. Thereby, the outer brain regions are subsequently removed in 2-mm layers parallel to the brain surface like ‘peeling an onion’. The serial convex planes enclosing the residual inner part of the brain are presented 3-dimensionally. If necessary (e.g., for intraoperative navigation), the normalized data can be transferred to native space by inverse normalization. Compared to cross-sectional images, curvilinear reformatting offers a markedly superior visualization of topographic relations between lesions and cortical structures, helps to detect subtle cortical malformations and to assess the spatial extent of lesions, thus allowing a better planning of neurosurgical procedures. Compared to alternative methods, it is largely based on freely available software and does not require observer-dependent manual input. In conclusion, we present a simple, easy-to-use and fully automated method for curvilinear reformatting of 3D MRI. [Copyright &y& Elsevier]

Malformations of cortical development (MCD) are a recognized cause of epilepsy. Their special significance lies in the fact that, once detected and delineated, they are amenable to surgical removal. However, diagnosis from high-resolution MRI is still difficult, time-consuming, and highly dependent on individual expertise. We have recently proposed a simple procedure to detect cortical dysplasias, using automated procedures available within SPM99 (Wellcome Department, University College London, UK). Here, we aimed to systematically determine the best combination of processing parameters, using an optimized voxel-based morphometry approach. We included 20 patients with a known MCD and compared them to a normal database of 53 healthy, age- and gender-matched controls. The approaches taken during spatial normalization and a number of other parameters were systematically altered in order to find the best combination of parameters. Overall, 99 different approaches were evaluated in different ways. As far as possible, automatic processing and evaluation steps were used. With the number of candidate regions for MCD limited to five per patient, the best approaches resulted in the correct identification of up to 16 of 20 malformations. However, a number of approaches failed to perform well. The reasons for these failures and the implications this has for other studies are discussed. We conclude that voxel-based morphometry is able to detect cortical malformations with a high degree of accuracy. However, specific problems seem to arise when using an optimized protocol for voxel-based morphometry, indicating that this protocol may not be optimal for all voxel-based studies on brain morphology. Our approach, involving systematic alterations of parameters and evaluation, may be useful for other studies. [Copyright &y& Elsevier]