A fast, model-independent method for cerebral cortical thickness estimation using MRI

Abstract

Several algorithms for measuring the cortical thickness in the human brain from MR image volumes have been described in the literature, the majority of which rely on fitting deformable models to the inner and outer cortical surfaces. However, the constraints applied during the model fitting process in order to enforce spherical topology and to fit the outer cortical surface in narrow sulci, where the cerebrospinal fluid (CSF) channel may be obscured by partial voluming, may introduce bias in some circumstances, and greatly increase the processor time required. In this paper we describe an alternative, voxel based technique that measures the cortical thickness using inversion recovery anatomical MR images. Grey matter, white matter and CSF are identified through segmentation, and edge detection is used to identify the boundaries between these tissues. The cortical thickness is then measured along the local 3D surface normal at every voxel on the inner cortical surface. The method was applied to 119 normal volunteers, and validated through extensive comparisons with published measurements of both cortical thickness and rate of thickness change with age. We conclude that the proposed technique is generally faster than deformable model-based alternatives, and free from the possibility of model bias, but suffers no reduction in accuracy. In particular, it will be applicable in data sets showing severe cortical atrophy, where thinning of the gyri leads to points of high curvature, and so the fitting of deformable models is problematic.

A comparison of voxel and surface based cortical thickness estimation methods

“…Ensuring correct topology or surface regularity massively increases computational cost (Fischl et al, 2001;Han et al, 2004), may require a difficult balance of parameter weights (Kim et al, 2005;Scott et al, 2009), and reduces the model's ability to follow areas of high curvature such as extremely thin gyral stalks (Lohmann et al, 2003) or opposing sides of sulci with no clear CSF between, which can produce bias and error in thickness measurements (Scott et al, 2009). …”

“…In contrast, voxel based methods (Lohmann et al, 2003;Hutton et al, 2008;Acosta et al, 2009;Aganj et al, 2009;Das et al, 2009;Cardoso et al, 2011;Scott et al, 2009) work directly on the voxel grid and are computationally very efficient. However, they are considered to be less accurate due to the limited resolution of the voxel grid, less robust to noise and mis-segmentation and significantly affected by partial volume (PV) effects at the boundaries of convoluted structures such as deep sulci (Acosta et al, 2009).…”

“…Methods include morphological (Lohmann et al, 2003), line integral (Aganj et al, 2009;Scott et al, 2009), Laplacian (Jones et al, 2000) and registration (Das et al, 2009) based approaches. Laplacian approaches (Hutton et al, 2008;Acosta et al, 2009;Cardoso et al, 2011), solve the Laplace equation (Jones et al, 2000) using boundary value relaxation (Press et al, 1991) or matrix methods (Haidar et al, 2005), calculate thickness by integrating the tangent to the Laplacian scalar field (Jones et al, 2000), summing the Euclidean distance from neighbouring voxels on the same streamline, or using a partial differential equation (Yezzi and Prince, 2003) with boundaries set to zero (Yezzi and Prince, 2003), half the mean voxel dimension (Diep et al, 2007) or using Lagrangian initialisation (Bourgeat et al, 2008;Acosta et al, 2009).…”

“…1 http://www.loni.ucla.edu/Software/BrainSuite 2 http://brainvisa.info/ 3 http://surfer.nmr.mgh.harvard.edu/ Recently there has been significant interest in the development of voxel based methods (Hutton et al, 2008;Scott et al, 2009;Acosta et al, 2009;Aganj et al, 2009;Cardoso et al, 2011;Das et al, 2009). In addition, voxel based methods have featured in a comparison with voxel based morphometry (Hutton et al, 2009), been used to correlate changes of cortical thickness with diffusion measures using sparse canonical correlation analysis (Avants et al, 2010) and used in clinical studies (Querbes et al, 2009).…”

Longitudinal Cortical Thickness Estimation Using Khalimsky’s Cubic Complex

“…Surface based methods [2] fit a triangulated mesh to the cerebral cortex, making them computationally expensive, especially due to topological constraints. Also, the parametrisation of the surface can be complex and curvature constraints and smoothness parameters can bias the thickness measurements [3]. Voxel-based methods on the other hand extract the value of thickness directly from the voxel grid and are computationally very efficient, but their accuracy is limited by the image resolution and the quality of the segmentation.…”