Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI

Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.

[1]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

[2]  Mark J. Lowe,et al.  An objective method for regularization of fiber orientation distributions derived from diffusion-weighted MRI , 2007, NeuroImage.

[3]  A. Anderson Measurement of fiber orientation distributions using high angular resolution diffusion imaging , 2005, Magnetic resonance in medicine.

[4]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[5]  D. Tuch Diffusion MRI of complex tissue structure , 2002 .

[6]  Carl-Fredrik Westin,et al.  A Bayesian approach for stochastic white matter tractography , 2006, IEEE Transactions on Medical Imaging.

[7]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[8]  C. D. Stern,et al.  The Human Central Nervous System: A Synopsis and Atlas, 3rd edition R. Nieuwenhuys, J. Voogd and C. van Huijzen. ISBN 0-387-13441-7. Price: $49.00. Springer, Berlin, 1988 , 1990, Neurochemistry International.

[9]  Rachid Deriche,et al.  Deterministic and Probabilistic Q-Ball Tractography: from Diffusion to Sharp Fiber Distributions , 2007 .

[10]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[11]  Daniel C. Alexander,et al.  Maximum Entropy Spherical Deconvolution for Diffusion MRI , 2005, IPMI.

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

[13]  Ching Yao,et al.  Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms , 2003, NeuroImage.

[14]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[15]  Steven W. Zucker,et al.  Trace Inference, Curvature Consistency, and Curve Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Jan Voogd,et al.  The human central nervous system : a synopsis and atlas , 1978 .

[17]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[18]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[19]  Rachid Deriche,et al.  Deterministic and Probabilistic Q-Ball Tractography: from Diffusion to Sharp Fiber Distribution , 2007 .

[21]  Daniel C Alexander,et al.  Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[22]  D. Tuch Q‐ball imaging , 2004, Magnetic resonance in medicine.

[23]  D. Le Bihan,et al.  Artifacts and pitfalls in diffusion MRI , 2006, Journal of magnetic resonance imaging : JMRI.

[24]  J M Taveras,et al.  Magnetic Resonance in Medicine , 1991, The Western journal of medicine.

[25]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[26]  Mariano Rivera,et al.  Basis Tensor Decomposition for Restoring Intra-Voxel Structure and Stochastic Walks for Inferring Brain Connectivity in DT-MRI , 2006, International Journal of Computer Vision.

[27]  Peter Savadjiev,et al.  3D curve inference for diffusion MRI regularization and fibre tractography , 2006, Medical Image Anal..

[28]  Baba C. Vemuri,et al.  A Unified Computational Framework for Deconvolution to Reconstruct Multiple Fibers From Diffusion Weighted MRI , 2007, IEEE Transactions on Medical Imaging.

[29]  Kalvis M. Jansons,et al.  Persistent angular structure: new insights from diffusion magnetic resonance imaging data , 2003 .

[30]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[31]  Philip A. Cook,et al.  A general framework for multiple-fibre PICo tractography , 2006 .

[32]  Abbas F. Sadikot,et al.  Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques , 2005, NeuroImage.

[33]  Thomas R. Knösche,et al.  Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging , 2007, NeuroImage.

[34]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[35]  Peter Savadjiev,et al.  Validation and regularization in diffusion MRI tractography , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[36]  V. Wedeen,et al.  Mapping fiber orientation spectra in cerebral white matter with Fourier-transform diffusion MRI , 2000 .

[37]  H. Pfeifer Principles of Nuclear Magnetic Resonance Microscopy , 1992 .

[38]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[39]  T. L. James,et al.  CHAPTER 2 – PRINCIPLES OF NUCLEAR MAGNETIC RESONANCE , 1975 .

[40]  Baba C. Vemuri,et al.  Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT) , 2006, NeuroImage.

[41]  Giuseppe Scotti,et al.  A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging , 2007, IEEE Transactions on Biomedical Engineering.

[42]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  R. Deriche,et al.  Regularized, fast, and robust analytical Q‐ball imaging , 2007, Magnetic resonance in medicine.