Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis

Quantitative diffusion tensor imaging (DTI) has become the major imaging modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics of tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.

[1]  Paul A. Yushkevich,et al.  Registration of diffusion tensor images , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[2]  Derek K. Jones,et al.  Spatial Normalization and Averaging of Diffusion Tensor MRI Data Sets , 2002, NeuroImage.

[3]  Guido Gerig,et al.  Towards a shape model of white matter fiber bundles using diffusion tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[4]  Carl-Fredrik Westin,et al.  Processing and visualization for diffusion tensor MRI , 2002, Medical Image Anal..

[5]  John H. Gilmore,et al.  3 Tesla magnetic resonance imaging of the brain in newborns , 2004, Psychiatry Research: Neuroimaging.

[6]  H. Moser,et al.  Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking , 2002, Magnetic resonance in medicine.

[7]  A. Anderson,et al.  Classification and quantification of neuronal fiber pathways using diffusion tensor MRI , 2003, Magnetic resonance in medicine.

[8]  M. Fréchet.  Les éléments aléatoires de nature quelconque dans un espace distancié , 1948 .

[9]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[10]  Isabelle Bloch,et al.  Towards inference of human brain connectivity from MR diffusion tensor data , 2001, Medical Image Anal..

[11]  Derek K. Jones,et al.  Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia , 2006, Human brain mapping.

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

[13]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[14]  Simon R. Arridge,et al.  Diffusion tensor magnetic resonance image regularization , 2004, Medical Image Anal..

[15]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[16]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[17]  Xavier Pennec,et al.  Probabilities and statistics on Riemannian manifolds: Basic tools for geometric measurements , 1999, NSIP.

[18]  J. Gee,et al.  Registration of diffusion tensor images , 2004, CVPR 2004.

[19]  Guido Gerig,et al.  Analysis Tool for Diffusion Tensor MRI , 2003, MICCAI.

[20]  Christos Davatzikos,et al.  A Framework for Callosal Fiber Distribution Analysis , 2002, NeuroImage.

[21]  Nicholas Ayache,et al.  Fast and Simple Calculus on Tensors in the Log-Euclidean Framework , 2005, MICCAI.

[22]  J. Schnabel,et al.  Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging , 2000, Journal of magnetic resonance imaging : JMRI.

[23]  James C. Gee,et al.  Spatial transformations of diffusion tensor magnetic resonance images , 2001, IEEE Transactions on Medical Imaging.

[24]  Guido Gerig,et al.  Quantitative Analysis of Diffusion Properties of White Matter Fiber Tracts: A Validation Study , 2005 .

[25]  Guido Gerig,et al.  A Statistical Shape Model of Individual Fiber Tracts Extracted from Diffusion Tensor MRI , 2004, MICCAI.

[26]  Maher Moakher,et al.  A rigorous framework for diffusion tensor calculus , 2005, Magnetic resonance in medicine.

[27]  A. Alexander,et al.  Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. , 2004, AJNR. American journal of neuroradiology.

[28]  J. Helpern,et al.  Neuropsychiatric applications of DTI – a review , 2002, NMR in biomedicine.

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

[30]  C. Goodall.  Procrustes methods in the statistical analysis of shape , 1991 .

[31]  John H. Gilmore,et al.  Quantitative Analysis of White Matter Fiber Properties along Geodesic Paths , 2003, MICCAI.

[32]  J. R.,et al.  Quantitative analysis , 1892, Nature.

[33]  S. Helgason.  Differential Geometry, Lie Groups, and Symmetric Spaces , 1978 .

[34]  Ruzena Bajcsy,et al.  Similarity Measures for Matching Diffusion Tensor Images , 1999, BMVC.

[35]  James Smith.  Table of Contents. , 2016, Journal of primary health care.

[36]  Mary A. Rutherford,et al.  MRI of the Neonatal Brain , 2001 .

[37]  P. Thomas Fletcher,et al.  Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors , 2004, ECCV Workshops CVAMIA and MMBIA.

[38]  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.