Detection of structural changes of the human brain in longitudinally acquired MR images by deformation field morphometry: Methodological analysis, validation and application

The progression of neurodegenerative diseases as well as healthy aging is accompanied by structural changes of the brain. These changes are often only subtle when considered over time intervals of several months. Therefore morphometrical techniques for their detection in longitudinally acquired MR images must be highly sensitive, and they require a careful validation. In the present study, a novel processing chain for a longitudinal analysis based on deformation field morphometry is described. Procedures for its quantitative validation are also reported: Deformation fields were computed for the simulation of non-linear, local structural changes of human brains. Applying these deformation fields to "original" MR images yielded deformed MR images. The volume changes defined by the deformation fields represented the standard, against which the results of the longitudinal analysis of each pair of original and deformed MR image were compared. The proposed processing chain enabled to localize and to quantify simulated local atrophies near the cortex as well as in deep brain structures. An exemplary analysis of serial MR images of a patient suffering from an atypical Parkinson syndrome (cortico-basal degeneration, CBD) and healthy control subjects is presented, showing a characteristic pattern of volume changes in the brain of the patient which is strikingly different from the controls' patterns of changes.

[1]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[2]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[3]  Christos Davatzikos,et al.  Simulation of tissue atrophy using a topology preserving transformation model , 2006, IEEE Transactions on Medical Imaging.

[4]  Cheryl L. Dahle,et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. , 2005, Cerebral cortex.

[5]  Dinggang Shen,et al.  Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping , 2004, NeuroImage.

[6]  Alan C. Evans,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2000, Nature.

[7]  Nick C. Fox,et al.  The application of serial MRI analysis techniques to the study of cerebral atrophy in late-onset dementia , 2004, Medical Image Anal..

[8]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[9]  Nick C Fox,et al.  Accurate registration of serial 3D MR brain images and its application to visualizing change in neurodegenerative disorders. , 1996, Journal of computer assisted tomography.

[10]  Norbert Schuff,et al.  Longitudinal stability of MRI for mapping brain change using tensor-based morphometry , 2006, NeuroImage.

[11]  K Amunts,et al.  Quantitative analysis of sulci in the human cerebral cortex: Development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture , 1997, Human brain mapping.

[12]  J. P. Brandel,et al.  Accuracy of the Clinical Diagnosis of Corticobasal Degeneration , 1997, Neurology.

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

[14]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[15]  Nick C. Fox,et al.  The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI , 1997, IEEE Transactions on Medical Imaging.

[16]  P. Matthews,et al.  Normalized Accurate Measurement of Longitudinal Brain Change , 2001, Journal of computer assisted tomography.

[17]  Christos Davatzikos,et al.  Voxel-Based Morphometry Using the RAVENS Maps: Methods and Validation Using Simulated Longitudinal Atrophy , 2001, NeuroImage.

[18]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[19]  J. Hajnal,et al.  Detection of Subtle Brain Changes Using Subvoxel Registration and Subtraction of Serial MR Images , 1995, Journal of computer assisted tomography.

[20]  Nick C Fox,et al.  A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. , 2003, Archives of neurology.

[21]  Nick C. Fox,et al.  Phenomenological Model of Diffuse Global and Regional Atrophy Using Finite-Element Methods , 2006, IEEE Transactions on Medical Imaging.

[22]  Stefan Henn,et al.  Iterative Multigrid Regularization Techniques for Image Matching , 2001, SIAM J. Sci. Comput..

[23]  Nick C Fox,et al.  Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. , 1998, Journal of computer assisted tomography.

[24]  Torsten Rohlfing,et al.  Deformation-based brain morphometry to track the course of alcoholism: Differences between intra-subject and inter-subject analysis , 2006, Psychiatry Research: Neuroimaging.

[25]  S. Resnick,et al.  Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain , 2003, The Journal of Neuroscience.

[26]  Armando Manduca,et al.  Methodological considerations for measuring rates of brain atrophy , 2003, Journal of magnetic resonance imaging : JMRI.

[27]  Alan C. Evans,et al.  A Unified Statistical Approach to Deformation-Based Morphometry , 2001, NeuroImage.

[28]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[29]  R W Baloh,et al.  Brain Volume Changes on Longitudinal Magnetic Resonance Imaging in Normal Older People , 2001, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[30]  Christos Davatzikos,et al.  Estimating topology preserving and smooth displacement fields , 2004, IEEE Transactions on Medical Imaging.

[31]  Irene Litvan,et al.  Corticobasal degeneration and its relationship to progressive supranuclear palsy and frontotemporal dementia , 2003, Annals of neurology.

[32]  Louis Lemieux,et al.  The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization , 1998, Medical Image Anal..

[33]  Emma B. Lewis,et al.  Correction of differential intensity inhomogeneity in longitudinal MR images , 2004, NeuroImage.

[34]  Daniel Rueckert,et al.  Cerebral atrophy measurements using Jacobian integration: Comparison with the boundary shift integral , 2006, NeuroImage.

[35]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.

[36]  Dinggang Shen,et al.  Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms , 2006, NeuroImage.

[37]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[38]  Lars Hömke,et al.  A multigrid method for anisotropic PDEs in elastic image registration , 2006, Numer. Linear Algebra Appl..

[39]  Nick C Fox,et al.  Brain atrophy progression measured from registered serial MRI: Validation and application to alzheimer's disease , 1997, Journal of magnetic resonance imaging : JMRI.

[40]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[41]  Colin Studholme,et al.  Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change , 2006, IEEE Transactions on Medical Imaging.

[42]  K O Lim,et al.  Progressive brain volume changes and the clinical course of schizophrenia in men: a longitudinal magnetic resonance imaging study. , 2001, Archives of general psychiatry.

[43]  S. Kiebel,et al.  Detecting Structural Changes in Whole Brain Based on Nonlinear Deformations—Application to Schizophrenia Research , 1999, NeuroImage.