A comparison of voxel and surface based cortical thickness estimation methods

Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.

[1]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[2]  C. Economo,et al.  Atlas of Cytoarchitectonics of the Adult Human Cerebral Cortex , 2008 .

[3]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[4]  Brian B. Avants,et al.  Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis , 2010, NeuroImage.

[5]  A. McKinney,et al.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease , 2010 .

[6]  Marc Modat,et al.  LoAd: A locally adaptive cortical segmentation algorithm , 2011, NeuroImage.

[7]  Isabelle Bloch,et al.  From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations , 1995, Journal of Mathematical Imaging and Vision.

[8]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[9]  Nick C Fox,et al.  Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral , 2007, Neurobiology of Aging.

[10]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[11]  Sébastien Ourselin,et al.  Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images , 2000, MICCAI.

[12]  Nick C Fox,et al.  Combining short interval MRI in Alzheimer’s disease , 2006, Journal of Neurology.

[13]  Sébastien Ourselin,et al.  EFFICIENT USE OF CEREBRAL CORTICAL THICKNESS TO CORRECT BRAIN MR SEGMENTATION , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  Christophe Lenglet,et al.  ODF reconstruction in q-ball imaging with solid angle consideration , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[16]  Sébastien Ourselin,et al.  Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian-Eulerian PDE approach using partial volume maps , 2009, Medical Image Anal..

[17]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[18]  M. Weiner,et al.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* , 2009, Brain : a journal of neurology.

[19]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  Alan C. Evans,et al.  Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.

[22]  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.

[23]  Robert Fox,et al.  CLADA: Cortical longitudinal atrophy detection algorithm , 2011, NeuroImage.

[24]  KJ Worsley,et al.  SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory , 2009, NeuroImage.

[25]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[26]  Alan C. Evans,et al.  Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.

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

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[30]  Nick C Fox,et al.  Acceleration of cortical thinning in familial Alzheimer's disease , 2011, Neurobiology of Aging.

[31]  T. Grabowski.  Semantic dementia and fluent primary progressive aphasia: two sides of the same coin? , 2008 .

[32]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[33]  Nick C. Fox,et al.  Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer's disease images , 2008, NeuroImage.

[34]  J. Pariente,et al.  Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve , 2009, Brain : a journal of neurology.

[35]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[36]  Brian B. Avants,et al.  Registration based cortical thickness measurement , 2009, NeuroImage.

[37]  Jerry L. Prince,et al.  Reconstruction of the human cerebral cortex from magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[38]  Nick C. Fox,et al.  Patterns of Cortical Thickness according to APOE Genotype in Alzheimer’s Disease , 2009, Dementia and Geriatric Cognitive Disorders.

[39]  Haissam Haidar,et al.  New Numerical Solution of the Laplace Equation for Tissue Thickness Measurement in Three-Dimensional MRI , 2005, J. Math. Model. Algorithms.

[40]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[41]  Nikolaus Weiskopf,et al.  A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging , 2009, NeuroImage.

[42]  Neil A. Thacker,et al.  A fast, model-independent method for cerebral cortical thickness estimation using MRI , 2009, Medical Image Anal..

[43]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[44]  Guillermo Sapiro,et al.  Measurement of cortical thickness from MRI by minimum line integrals on soft‐classified tissue , 2009, Human brain mapping.

[45]  C. Davatzikos,et al.  Using a deformable surface model to obtain a shape representation of the cortex , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[46]  Nick C Fox,et al.  Increased hippocampal atrophy rates in AD over 6 months using serial MR imaging , 2008, Neurobiology of Aging.

[47]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative , 2008 .

[48]  R G Newcombe,et al.  Improved confidence intervals for the difference between binomial proportions based on paired data. , 1998, Statistics in medicine.

[49]  A. Dale,et al.  Thinning of the cerebral cortex in aging. , 2004, Cerebral cortex.

[50]  M N Rossor,et al.  Patterns of temporal lobe atrophy in semantic dementia and Alzheimer's disease , 2001, Annals of neurology.

[51]  Robert Turner,et al.  Voxel-based cortical thickness measurements in MRI , 2008, NeuroImage.

[52]  Xiao Han,et al.  CRUISE: Cortical reconstruction using implicit surface evolution , 2004, NeuroImage.

[53]  Marc Modat,et al.  Atrophy patterns in Alzheimer's disease and semantic dementia: A comparison of FreeSurfer and manual volumetric measurements , 2010, NeuroImage.

[54]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[55]  Nick C Fox,et al.  Reduced cortical thickness in the posterior cingulate gyrus is characteristic of both typical and atypical Alzheimer's disease. , 2010, Journal of Alzheimer's disease : JAD.

[56]  A. Dale,et al.  Focal thinning of the cerebral cortex in multiple sclerosis. , 2003, Brain : a journal of neurology.

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

[58]  Bruce Fischl,et al.  Cerebral cortex and the clinical expression of Huntington's disease: complexity and heterogeneity. , 2008, Brain : a journal of neurology.

[59]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[60]  Richard M. Leahy,et al.  Automated graph-based analysis and correction of cortical volume topology , 2001, IEEE Transactions on Medical Imaging.

[61]  Michael Weiner,et al.  Automated MRI-based classification of primary progressive aphasia variants , 2009, NeuroImage.

[62]  Brian B. Avants,et al.  Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex , 2006, Medical Image Anal..

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

[64]  M N Rossor,et al.  Measuring atrophy in Alzheimer disease , 2005, Neurology.

[65]  A. Dale,et al.  Subregional neuroanatomical change as a biomarker for Alzheimer's disease , 2009, Proceedings of the National Academy of Sciences.

[66]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[67]  Alan C. Evans,et al.  A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom , 2006, NeuroImage.

[68]  Nick C Fox,et al.  Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration , 2009, Neurology.

[69]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[70]  Nick C Fox,et al.  Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer's disease , 2011, Neurobiology of Aging.

[71]  N. Schuff,et al.  Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. , 2006, Brain : a journal of neurology.

[72]  Sébastien Ourselin,et al.  Improved cortical thickness measurement from MR images using partial volume estimation , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[73]  W. Eric L. Grimson,et al.  A Genetic Algorithm for the Topology Correction of Cortical Surfaces , 2005, IPMI.

[74]  Richard M. Leahy,et al.  BrainSuite: An Automated Cortical Surface Identification Tool , 2000, MICCAI.

[75]  Kawal S. Rhode,et al.  Model-based respiratory motion correction using 3-D echocardiography , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.