MRtrix: Diffusion tractography in crossing fiber regions

In recent years, diffusion‐weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 53–66, 2012

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

[2]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[3]  Karl J. Friston,et al.  Non-invasive mapping of corticofugal fibres from multiple motor areas--relevance to stroke recovery. , 2006, Brain : a journal of neurology.

[4]  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.

[5]  J. Tournier The biophysics of crossing fibres , 2010 .

[6]  Daniel C Alexander,et al.  Multiple‐Fiber Reconstruction Algorithms for Diffusion MRI , 2005, Annals of the New York Academy of Sciences.

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

[8]  Guy B. Williams,et al.  Inference of multiple fiber orientations in high angular resolution diffusion imaging , 2005, Magnetic resonance in medicine.

[9]  Alan Connelly,et al.  Super-resolution track-density imaging studies of mouse brain: Comparison to histology , 2012, NeuroImage.

[10]  David G. Norris,et al.  An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging , 2002, NeuroImage.

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

[12]  P. Basser Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images , 1995, NMR in biomedicine.

[13]  L. Frank Anisotropy in high angular resolution diffusion‐weighted MRI , 2001, Magnetic resonance in medicine.

[14]  Rachid Deriche,et al.  Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions , 2009, IEEE Transactions on Medical Imaging.

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

[16]  Kerstin Pannek,et al.  Comparative mouse brain tractography of diffusion magnetic resonance imaging , 2010, NeuroImage.

[17]  J. Dubois,et al.  Optimized diffusion gradient orientation schemes for corrupted clinical DTI data sets , 2006, Magnetic Resonance Materials in Physics, Biology and Medicine.

[18]  Giuseppe Scotti,et al.  Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas , 2008, NeuroImage.

[19]  Martin Styner,et al.  FADTTS: Functional analysis of diffusion tensor tract statistics , 2011, NeuroImage.

[20]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Daniel C. Alexander,et al.  Using the Model-Based Residual Bootstrap to Quantify Uncertainty in Fiber Orientations From $Q$-Ball Analysis , 2009, IEEE Transactions on Medical Imaging.

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

[23]  Carlo Pierpaoli,et al.  Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach , 2005, Magnetic resonance in medicine.

[24]  Manabu Kinoshita,et al.  Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation , 2005, NeuroImage.

[25]  Daniel C. Alexander,et al.  Linear Persistent Angular Structure MRI and non-linear Spherical Deconvolution for Diffusion MRI , 2006 .

[26]  D. Tuch High Angular Resolution Diffusion Imaging of the Human Brain , 1999 .

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

[28]  Alan Connelly,et al.  Diffusion-weighted magnetic resonance imaging fibre tracking using a front evolution algorithm , 2003, NeuroImage.

[29]  N. Makris,et al.  High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.

[30]  D. Parker,et al.  Analysis of partial volume effects in diffusion‐tensor MRI , 2001, Magnetic resonance in medicine.

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

[32]  Chun-Hung Yeh,et al.  Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data , 2008, NeuroImage.

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

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

[35]  Jacques-Donald Tournier,et al.  Diffusion tensor imaging and beyond , 2011, Magnetic resonance in medicine.

[36]  Alan Connelly,et al.  Track density imaging (TDI): Validation of super resolution property , 2011, NeuroImage.

[37]  D G Gadian,et al.  Limitations and requirements of diffusion tensor fiber tracking: An assessment using simulations , 2002, Magnetic resonance in medicine.

[38]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.

[39]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[40]  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.

[41]  Geoffrey J M Parker,et al.  A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements , 2003, Journal of magnetic resonance imaging : JMRI.

[42]  Stuart Crozier,et al.  Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images , 2012, NeuroImage.

[43]  Gareth J. Barker,et al.  Optimal imaging parameters for fiber-orientation estimation in diffusion MRI , 2005, NeuroImage.

[44]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[45]  Derek K. Jones,et al.  Virtual in Vivo Interactive Dissection of White Matter Fasciculi in the Human Brain , 2002, NeuroImage.

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

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

[48]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[49]  Yihong Yang,et al.  How accurately can the diffusion profiles indicate multiple fiber orientations? A study on general fiber crossings in diffusion MRI. , 2006, Journal of magnetic resonance.

[50]  M. Catani,et al.  Diffusion-based tractography in neurological disorders: concepts, applications, and future developments , 2008, The Lancet Neurology.

[51]  Heidi Johansen-Berg,et al.  Using diffusion imaging to study human connectional anatomy. , 2009, Annual review of neuroscience.

[52]  Matt Hall,et al.  Resolving axon fiber crossings at clinical b-values: an evaluation study. , 2011, Medical physics.

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

[54]  A Thron,et al.  Functional and diffusion-weighted magnetic resonance imaging for visualization of the postthalamic visual fiber tracts and the visual cortex. , 2004, Minimally invasive neurosurgery : MIN.

[55]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[56]  Carlo Pierpaoli,et al.  PASTA: Pointwise assessment of streamline tractography attributes , 2005, Magnetic resonance in medicine.

[57]  Alan Connelly,et al.  Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping , 2010, NeuroImage.

[58]  Chris A Clark,et al.  White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning? , 2003, NeuroImage.

[59]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[60]  Christopher Nimsky,et al.  Prediction of visual field deficits by diffusion tensor imaging in temporal lobe epilepsy surgery , 2009, NeuroImage.

[61]  Kenji Ino,et al.  Outcomes of diffusion tensor tractography-integrated stereotactic radiosurgery. , 2012, International journal of radiation oncology, biology, physics.

[62]  Jeremy D. Schmahmann,et al.  Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers , 2008, NeuroImage.

[63]  S. Arridge,et al.  Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data , 2002, Magnetic resonance in medicine.

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

[65]  Derek K. Jones,et al.  Estimating the number of fiber orientations in diffusion MRI voxels : a constrained spherical deconvolution study , 2010 .

[66]  Gareth J. Barker,et al.  Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging , 2002, IEEE Transactions on Medical Imaging.

[67]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[68]  Jan Sijbers,et al.  Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution , 2011, Human brain mapping.

[69]  Andrew L. Alexander,et al.  Bootstrap white matter tractography (BOOT-TRAC) , 2005, NeuroImage.

[70]  P. Bhattacharya Diffusion MRI: Theory, methods, and applications, Derek K. Jones (Ed.). Oxford University press (2011), $152.77 , 2012 .

[71]  F. Calamante,et al.  How many diffusion gradient directions are required for HARDI , 2009 .

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