DTI Analysis Methods: Voxel-Based Analysis

Voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data permits the investigation of voxel-wise differences or changes in DTI metrics in every voxel of a brain dataset. It is applied primarily in the exploratory analysis of hypothesized group-level alterations in DTI parameters, as it does not require prior knowledge of where in the brain such changes may occur. Whilst VBA is a widely used, powerful preclinical research tool, there are a number of methodological issues that should be considered when applying the technique to study (pre)clinical populations. This chapter reviews the component steps of a typical VBA study pipeline and includes a comprehensive introduction to image registration, DTI template/atlas selection, smoothing, and statistical analysis. The popular tract-based spatial (TBSS) technique is introduced and contrasted with traditional VBA approaches. At each stage, guidance on optimizing parameter settings is presented along with the pros and cons of different methods to assist the reader in choosing the best approach for their application.

[1]  Konstantinos Arfanakis,et al.  Enhanced ICBM diffusion tensor template of the human brain , 2011, NeuroImage.

[2]  Marc Modat,et al.  The Importance of Group-Wise Registration in Tract Based Spatial Statistics Study of Neurodegeneration: A Simulation Study in Alzheimer's Disease , 2012, PloS one.

[3]  Daniel Rueckert,et al.  Unbiased White Matter Atlas Construction Using Diffusion Tensor Images , 2007, MICCAI.

[4]  Dinggang Shen,et al.  TIMER: Tensor Image Morphing for Elastic Registration , 2009, NeuroImage.

[5]  Derek K. Jones,et al.  Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor MR imaging data , 2011, Journal of Neuroscience Methods.

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

[7]  Dinggang Shen,et al.  F-TIMER: Fast Tensor Image Morphing for Elastic Registration , 2010, IEEE Transactions on Medical Imaging.

[8]  Alan C. Evans,et al.  A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[9]  Michael L. Lipton,et al.  Whole Brain Approaches for Identification of Microstructural Abnormalities in Individual Patients: Comparison of Techniques Applied to Mild Traumatic Brain Injury , 2013, PloS one.

[10]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[11]  James C. Gee,et al.  Elastic Matching of Diffusion Tensor Images , 2000, Comput. Vis. Image Underst..

[12]  Scott H Faro,et al.  Application of voxelwise analysis in the detection of regions of reduced fractional anisotropy in multiple sclerosis patients , 2007, Journal of magnetic resonance imaging : JMRI.

[13]  Carl-Fredrik Westin,et al.  Deformable registration of DT-MRI data based on transformation invariant tensor characteristics , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[14]  Stephen T. C. Wong,et al.  Simultaneous Consideration of Spatial Deformation and Tensor Orientation in Diffusion Tensor Image Registration Using Local Fast Marching Patterns , 2009, IPMI.

[15]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[16]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[17]  Jan Sijbers,et al.  The effect of template selection on diffusion tensor voxel-based analysis results , 2011, NeuroImage.

[18]  J Sijbers,et al.  Multiscale white matter fiber tract coregistration: A new feature‐based approach to align diffusion tensor data , 2006, Magnetic resonance in medicine.

[19]  Jan Sijbers,et al.  On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain , 2008, NeuroImage.

[20]  Gady Agam,et al.  Development of a human brain diffusion tensor template , 2009, NeuroImage.

[21]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[22]  Moo K. Chung,et al.  A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis , 2009, NeuroImage.

[23]  John H. Gilmore,et al.  Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building , 2006, MICCAI.

[24]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[25]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[26]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[27]  Andrew Zalesky,et al.  Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection. , 2011, Magnetic resonance imaging.

[28]  Konstantinos Arfanakis,et al.  Role of standardized and study‐specific human brain diffusion tensor templates in inter‐subject spatial normalization , 2013, Journal of magnetic resonance imaging : JMRI.

[29]  Derek K. Jones,et al.  The effect of filter size on VBM analyses of DT-MRI data , 2005, NeuroImage.

[30]  Juan Ruiz-Alzola,et al.  Nonrigid registration of 3D tensor medical data , 2002, Medical Image Anal..

[31]  Jan Sijbers,et al.  Comparing isotropic and anisotropic smoothing for voxel‐based DTI analyses: A simulation study , 2009, Human brain mapping.

[32]  Carl-Fredrik Westin,et al.  Spatial normalization of diffusion tensor MRI using multiple channels , 2003, NeuroImage.

[33]  Jan Sijbers,et al.  Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information , 2007, IEEE Transactions on Medical Imaging.

[34]  Paul M. Thompson,et al.  Fluid Registration of Diffusion Tensor Images Using Information Theory , 2008, IEEE Transactions on Medical Imaging.

[35]  Michael L. Lipton,et al.  Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: Intersubject variation, change over time and bidirectional changes in anisotropy , 2012, Brain Imaging and Behavior.

[36]  G. Marchal,et al.  Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: Revisited , 2009, Human brain mapping.

[37]  Paul M. Thompson,et al.  Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics , 2014, NeuroImage.

[38]  Carl-Fredrik Westin,et al.  Tract-based morphometry for white matter group analysis , 2009, NeuroImage.

[39]  Derek K. Jones,et al.  Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging , 2013, Human brain mapping.

[40]  Paul A. Yushkevich,et al.  Deformable registration of diffusion tensor MR images with explicit orientation optimization , 2006, Medical Image Anal..

[41]  Carlo Pierpaoli,et al.  A Comprehensive Approach for Multi-channel Image Registration , 2003, WBIR.