Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players . Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  Timothy D. Verstynen,et al.  Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.

[3]  D. Cohen-Or,et al.  Curve skeleton extraction from incomplete point cloud , 2009, SIGGRAPH 2009.

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

[5]  J. Mårtensson,et al.  Spatial analysis of diffusion tensor tractography statistics along the inferior fronto-occipital fasciculus with application in progressive supranuclear palsy , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.

[6]  Bernhard Schölkopf,et al.  BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI , 2017, Pattern Recognit..

[7]  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).

[8]  G. Weber The human brain and spinal cord: Functional neuroanatomy and dissection guide, 2nd edition by L. Heimer, 1995, Springer-Verlag, New York-Berlin-Heidelberg, 506 pages, DM 78.00, ISBN 0-387-94227-0 , 1996, Journal of the Neurological Sciences.

[9]  Heinz-Otto Peitgen,et al.  Automatic Quantification of DTI Parameters Along Fiber Bundles , 2007, Bildverarbeitung für die Medizin.

[10]  David Rousseau,et al.  A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis , 2016, PloS one.

[11]  R. Kikinis,et al.  A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury , 2012, Brain Imaging and Behavior.

[12]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Maxime Descoteaux,et al.  Robust and efficient linear registration of white-matter fascicles in the space of streamlines , 2015, NeuroImage.

[14]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[15]  Guy B. Williams,et al.  QuickBundles, a Method for Tractography Simplification , 2012, Front. Neurosci..

[16]  Laurent Younes,et al.  Computable Elastic Distances Between Shapes , 1998, SIAM J. Appl. Math..

[17]  Alain Trouvé,et al.  The Varifold Representation of Nonoriented Shapes for Diffeomorphic Registration , 2013, SIAM J. Imaging Sci..

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

[19]  Carl-Fredrik Westin,et al.  Unbiased Groupwise Registration of White Matter Tractography , 2012, MICCAI.

[20]  Lennart Heimer The Human Brain and Spinal Cord , 1983 .

[21]  Nagesh Adluru,et al.  Cosine series representation of 3D curves and its application to white matter fiber bundles in diffusion tensor imaging. , 2010, Statistics and its interface.

[22]  L. O'Donnell,et al.  Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls , 2015, Schizophrenia Research.

[23]  Nicholas Ayache,et al.  Tracking Points on Deformable Objects Using Curvature Information , 1992, ECCV.

[24]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[25]  Ronen Basri,et al.  Curve Matching Using the Fast Marching Method , 2003, EMMCVPR.

[26]  Paul M. Thompson,et al.  Along-tract statistics allow for enhanced tractography analysis , 2012, NeuroImage.

[27]  Paul A. Yushkevich,et al.  Structure-specific statistical mapping of white matter tracts , 2007, NeuroImage.

[28]  M. Hulkower,et al.  A Decade of DTI in Traumatic Brain Injury: 10 Years and 100 Articles Later , 2013, American Journal of Neuroradiology.

[29]  Alain Trouvé,et al.  Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents , 2011, NeuroImage.

[30]  B. Wandell,et al.  Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification , 2012, PloS one.

[31]  Philip N. Klein,et al.  On Aligning Curves , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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