12/23/2023 0 Comments Essential anatomy 3 windowsWe apply a multi-grid approach that uses reparameterized values of sulcal depth and shape index defined on the sphere to estimate a flow field that allows deforming a spherical grid. We have adapted the volume-based diffeomorphic Dartel algorithm to the surface ( Ashburner 2007) to work with spherical maps ( Yotter et al. We use a fast algorithm to reduce area distortion resulting in an improved reparameterization of the cortical surface mesh ( Yotter et al. Spherical mappingĪ spherical map of a cortical surface is usually necessary to reparameterize the surface mesh into a common coordinate system to allow inter-subject analysis. Then, a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. First, the original MRI intensity values are used as a basis to select either a ’fill’ or ’cut’ operation for each topological defect. In order to repair topological defects we use a novel method that relies on spherical harmonics ( Yotter et al. This projection-based thickness (PBT) allows the handling of partial volume information, sulcal blurring, and sulcal asymmetries without explicit sulcus reconstruction ( Dahnke et al. It uses a tissue segmentation to estimate the white matter (WM) distance, then projects the local maxima (which is equal to the cortical thickness) to other gray matter voxels by using a neighbor relationship described by the WM distance. We use a fully automated method that allows for measurement of cortical thickness and reconstructions of the central surface in one step. Cortical thickness and central surface estimation Furthermore, inflation or spherical mapping of the cortical surface mesh raises the buried sulci to the surface so that mapped functional activity in these regions can be easily visualized. ![]() 2011c), local gyrification ( Luders et al. Brain surface meshes also permit new forms of analyses, such as gyrification indices that measure surface complexity in 3D ( Yotter et al. For instance, brain surface meshes have been shown to increase the accuracy of brain registration compared with Talairach registration ( Desai et al. Surface-based morphometry has several advantages over using volumetric data alone. 2011a) and spherical mapping ( Yotter et al. ![]() 2012).įurthermore, the surface pipeline uses topology correction ( Yotter et al. NextĬAT12 additionally includes the estimation of the cortical thickness and central surface of the left and right hemispheres based on the projection-based thickness (PBT) method ( Dahnke et al. This approach is also known as tensor-based morphometry because the Jacobian determinant represents such a tensor. The Jacobian determinant allows a direct estimation of the percentage change in volume in each voxel and can be statistically analyzed ( Gaser et al. This parameter is well known from continuum mechanics and is usually used for the analysis of volume changes in flowing liquids or gases. Finally, a map of local volume changes can be quantified by a mathematical property of these deformations - the Jacobian determinant. If this local adaptation is possible, the deformations now reveal information about the type and localization of the structural differences between the brains and can undergo subsequent analysis.ĭifferences between both images are minimized and are now coded in the deformations. ![]() In addition to these global changes, a non-linear registration is necessary to minimise the remaining regional differences by means of local deformations. The simplest case of spatial registration is to correct the orientation and size of the brains. DBM is based on the application of non-linear registration procedures to spatially normalise one brain to another one.
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