Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach

AbstractThe recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics. Graphical Abstract

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

[2]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

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

[4]  Dimitris N. Metaxas,et al.  Meshless deformable models for 3D cardiac motion and strain analysis from tagged MRI. , 2015, Magnetic resonance imaging.

[5]  James V. Miller,et al.  A Method for Registering Diffusion Weighted Magnetic Resonance Images , 2006, MICCAI.

[6]  Maxime Descoteaux,et al.  Tractometer: Towards validation of tractography pipelines , 2013, Medical Image Anal..

[7]  Heye Zhang,et al.  A Meshfree Representation for Cardiac Medical Image Computing , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

[8]  Carl-Fredrik Westin,et al.  Probabilistic ODF Estimation from Reduced HARDI Data with Sparse Regularization , 2011, MICCAI.

[9]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..

[10]  Rachid Deriche,et al.  Impact of Rician Adapted Non-Local Means Filtering on HARDI , 2008, MICCAI.

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

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

[13]  W. Skrandies,et al.  Reconstruction of electroencephalographic data using radial basis functions , 2016, Clinical Neurophysiology.

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

[15]  Hartwig R. Siebner,et al.  Interpolation of diffusion weighted imaging datasets , 2014, NeuroImage.

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

[17]  M. Horsfield,et al.  Diffusion tensor MRI tractography and cognitive impairment in multiple sclerosis , 2012, Neurology.

[18]  Roland G. Henry,et al.  Probabilistic streamline q-ball tractography using the residual bootstrap , 2008, NeuroImage.

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

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

[21]  M. Styner,et al.  Investigating the tradeoffs between spatial resolution and diffusion sampling for brain mapping with diffusion tractography: Time well spent? , 2014, Human brain mapping.

[22]  Maxime Descoteaux,et al.  Collaborative patch-based super-resolution for diffusion-weighted images , 2013, NeuroImage.

[23]  Jean-Philippe Thiran,et al.  Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$ , 2012, 1208.2247.

[24]  Susumu Mori,et al.  Introduction to Diffusion Tensor Imaging , 2007 .

[25]  Rodney A. Kennedy,et al.  On the Use of Antipodal Optimal Dimensionality Sampling Scheme on the Sphere for Recovering Intra-Voxel Fibre Structure in Diffusion MRI , 2016 .

[26]  Bram Stieltjes,et al.  Fiberfox: Facilitating the creation of realistic white matter software phantoms , 2014, Magnetic resonance in medicine.

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

[28]  Dinggang Shen,et al.  Fiber-driven resolution enhancement of diffusion-weighted images , 2014, NeuroImage.

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

[30]  B. Pollock,et al.  Diffusion tensor tractography findings in schizophrenia across the adult lifespan. , 2010, Brain : a journal of neurology.

[31]  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.

[32]  Faouzi Ghorbel,et al.  A Novel Geometrical Approach for a Rapid Estimation of the HARDI Signal in Diffusion MRI , 2016, ICISP.

[33]  Toshiaki Taoka,et al.  Uncinate fasciculus‐correlated cognition in Alzheimer's disease: a diffusion tensor imaging study by tractography , 2010, Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society.

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

[35]  Maria Francesca Carfora.  Interpolation on spherical geodesic grids: A comparative study , 2007 .

[36]  Dinggang Shen,et al.  Designing Single- and Multiple-Shell Sampling Schemes for Diffusion MRI Using Spherical Code , 2014, MICCAI.

[37]  A. Connelly,et al.  Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging , 2013, NMR in biomedicine.

[38]  K. Trinkaus,et al.  Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis. , 2015, Brain : a journal of neurology.

[39]  Principle of diffusion tensor imaging , 2007 .

[40]  Ching-Po Lin,et al.  Potential in reducing scan times of HARDI by accurate correction of the cross‐term in a hemispherical encoding scheme , 2009, Journal of magnetic resonance imaging : JMRI.

[41]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

[42]  Katie L. McMahon,et al.  lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain , 2015, Front. Neurol..

[43]  R. Kikinis,et al.  Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning , 2011, Neurosurgery.

[44]  Faouzi Ghorbel,et al.  Fast and Accurate Estimation of the HARDI Signal in Diffusion MRI Using a Nearest-Neighbor Interpolation Approach , 2017 .

[45]  J. Thiran,et al.  Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[46]  Yogesh Rathi,et al.  Fast and Accurate Reconstruction of HARDI Data Using Compressed Sensing , 2010, MICCAI.

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