Measuring Cortical Thickness Using An Image Domain Local Surface Model And Topology Preserving Segmentation

We present a measure of gray matter (GM) thickness based on local surface models in the image domain. Thickness is measured by integrating GM probability maps along the white matter (WM) surface normal direction. The method is simple to implement and allows statistical tests to be performed in the gray matter volume. A novel topology preserving segmentation method is introduced that is able to accurately recover GM in deep sulci. We apply this methodology to a longitudinal study of gray matter atrophy in a patient cohort diagnosed with frontotemporal dementia (FTD) spectrum disorders. Following image-based normalization of GM thickness maps, results show significant reduction in cortical thickness in several Brodmann areas spanning temporal, parietal and frontal lobes across subjects.

[1]  Kiralee M. Hayashi,et al.  Abnormal Cortical Complexity and Thickness Profiles Mapped in Williams Syndrome , 2005, The Journal of Neuroscience.

[2]  Gabriele Lohmann,et al.  Morphology-Based Cortical Thickness Estimation , 2003, IPMI.

[3]  Jerry L. Prince,et al.  Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm , 2002, IEEE Transactions on Medical Imaging.

[4]  Alan C. Evans,et al.  Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. , 2004, Cerebral cortex.

[5]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[6]  J. Gee,et al.  Geodesic estimation for large deformation anatomical shape averaging and interpolation , 2004, NeuroImage.

[7]  Longin Jan Latecki,et al.  Making 3D binary digital images well-composed , 2005, IS&T/SPIE Electronic Imaging.

[8]  Neil A. Thacker,et al.  Robust Tissue Boundary Detection for Cerebral Cortical Thickness Estimation , 2005, MICCAI.

[9]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[10]  Jerry L. Prince,et al.  An Eulerian PDE approach for computing tissue thickness , 2003, IEEE Transactions on Medical Imaging.

[11]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Robert Turner,et al.  Sulcal Segmentation for Cortical Thickness Measurements , 2002, MICCAI.

[13]  Nick C Fox,et al.  Rates of global and regional cerebral atrophy in AD and frontotemporal dementia , 2001, Neurology.

[14]  P. Danielsson.  Euclidean distance mapping , 1980 .

[15]  J. Gee,et al.  What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration. , 2003, Brain : a journal of neurology.

[16]  Brian Avants,et al.  The Correlation of Cognitive Decline with Frontotemporal Dementia Induced Annualized Gray Matter Loss Using Diffeomorphic Morphometry , 2005, Alzheimer disease and associated disorders.

[17]  Brian B. Avants,et al.  The Shape Operator for Differential Analysis of Images , 2003, IPMI.

[18]  I. Aharon,et al.  Three‐dimensional mapping of cortical thickness using Laplace's Equation , 2000, Human brain mapping.

[19]  John Ashburner,et al.  A tensor based morphometry study of longitudinal gray matter contraction in FTD , 2007, NeuroImage.

[20]  Michael I. Miller,et al.  A stochastic model for studying the laminar structure of cortex from MRI , 2005, IEEE Transactions on Medical Imaging.

[21]  Brian B. Avants,et al.  Spatiotemporal Normalization for Longitudinal Analysis of Gray Matter Atrophy in Frontotemporal Dementia , 2007, MICCAI.

[22]  Patrick Dupont,et al.  An Automated 3D Algorithm for Neo-cortical Thickness Measurement , 2003, MICCAI.