Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the “HARDI reconstruction challenge” organized in the context of the “ISBI 2012” conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

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

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

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

[4]  Timo Dickscheid,et al.  High-Resolution Fiber Tract Reconstruction in the Human Brain by Means of Three-Dimensional Polarized Light Imaging , 2011, Front. Neuroinform..

[5]  Mark F. Lythgoe,et al.  Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison , 2012, NeuroImage.

[6]  Maxime Descoteaux,et al.  Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom , 2011, NeuroImage.

[7]  P. Hubbard,et al.  Coaxially electrospun axon-mimicking fibers for diffusion magnetic resonance imaging. , 2012, ACS applied materials & interfaces.

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

[9]  Yogesh Rathi,et al.  Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging , 2011, IEEE Transactions on Medical Imaging.

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

[11]  Carl-Fredrik Westin,et al.  Sparse Multi-Shell Diffusion Imaging , 2011, MICCAI.

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

[13]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[14]  Remco Duits,et al.  Extrapolating fiber crossings from DTI data : can we infer similar fiber crossings as in HARDI? , 2010 .

[15]  G. Sapiro,et al.  Reconstruction of the orientation distribution function in single‐ and multiple‐shell q‐ball imaging within constant solid angle , 2010, Magnetic resonance in medicine.

[16]  Lester Melie-García,et al.  A Bayesian framework to identify principal intravoxel diffusion profiles based on diffusion-weighted MR imaging , 2008, NeuroImage.

[17]  Kaleem Siddiqi,et al.  Recent advances in diffusion MRI modeling: Angular and radial reconstruction , 2011, Medical Image Anal..

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

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

[20]  B W Kreher,et al.  Multitensor approach for analysis and tracking of complex fiber configurations , 2005, Magnetic resonance in medicine.

[21]  Santiago Aja-Fern,et al.  A review on statistical noise models for Magnetic Resonance Imaging 1 , 2013 .

[22]  J. Mangin,et al.  New diffusion phantoms dedicated to the study and validation of high‐angular‐resolution diffusion imaging (HARDI) models , 2008, Magnetic resonance in medicine.

[23]  S. Holland,et al.  NMR relaxation times in the human brain at 3.0 tesla , 1999, Journal of magnetic resonance imaging : JMRI.

[24]  Mathews Jacob,et al.  Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[25]  Mariano Rivera,et al.  Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry , 2007, IEEE Transactions on Medical Imaging.

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

[27]  Bram Stieltjes,et al.  How background noise shifts eigenvectors and increases eigenvalues in DTI , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

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

[29]  Justin P. Haldar,et al.  Linear transforms for Fourier data on the sphere: Application to high angular resolution diffusion MRI of the brain , 2013, NeuroImage.

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

[31]  Carl-Fredrik Westin,et al.  Estimation of fiber Orientation Probability Density Functions in High Angular Resolution Diffusion Imaging , 2009, NeuroImage.

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

[33]  R. Goebel,et al.  Ground truth hardware phantoms for validation of diffusion‐weighted MRI applications , 2010, Journal of magnetic resonance imaging : JMRI.

[34]  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.

[35]  B. Stieltjes,et al.  Novel spherical phantoms for Q‐ball imaging under in vivo conditions , 2011, Magnetic resonance in medicine.

[36]  D. Pandya,et al.  Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. , 2007, Brain : a journal of neurology.

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

[38]  Jerry L. Prince,et al.  Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI , 2012, NeuroImage.

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

[40]  A. Dale,et al.  Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain , 2010, PloS one.

[41]  Giuseppe Scotti,et al.  A modified damped Richardson–Lucy algorithm to reduce isotropic background effects in spherical deconvolution , 2010, NeuroImage.

[42]  Maxime Descoteaux,et al.  Spherical wavelet transform for ODF sharpening , 2010, Medical Image Anal..

[43]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[44]  James C. Gee,et al.  A Comparison of Methods for Recovering Intra-voxel White Matter Fiber Architecture from Clinical Diffusion Imaging Scans , 2008, MICCAI.

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

[46]  A. W. Anderson,et al.  Sub-voxel measurement of fiber orientation using high angular resolution diffusion tensor imaging , 2002 .

[47]  R. Goebel,et al.  Histological validation of DW-MRI tractography in human postmortem tissue. , 2013, Cerebral cortex.

[48]  Baba C. Vemuri,et al.  Regularized positive-definite fourth order tensor field estimation from DW-MRI , 2009, NeuroImage.

[49]  Hans-Peter Seidel,et al.  Estimating Crossing Fibers: A Tensor Decomposition Approach , 2008, IEEE Transactions on Visualization and Computer Graphics.

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

[51]  P. Callaghan,et al.  Diffraction-like effects in NMR diffusion studies of fluids in porous solids , 1991, Nature.

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

[53]  Rachid Deriche,et al.  Parametric Dictionary Learning for Modeling EAP and ODF in Diffusion MRI , 2012, MICCAI.

[54]  Bram Stieltjes,et al.  On the effects of dephasing due to local gradients in diffusion tensor imaging experiments: relevance for diffusion tensor imaging fiber phantoms. , 2009, Magnetic resonance imaging.

[55]  B. M. ter Haar Romeny,et al.  Extrapolating fiber crossings from DTI data : can we gain the same information as HARDI? , 2010 .

[56]  Bram Stieltjes,et al.  Investigation of resolution effects using a specialized diffusion tensor phantom , 2014, Magnetic resonance in medicine.

[57]  Erick Jorge Canales-Rodríguez,et al.  Mathematical description of q‐space in spherical coordinates: Exact q‐ball imaging , 2009, Magnetic resonance in medicine.

[58]  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.

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

[60]  Fang-Cheng Yeh,et al.  Generalized ${ q}$-Sampling Imaging , 2010, IEEE Transactions on Medical Imaging.

[61]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[62]  Timothy Edward John Behrens,et al.  Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE , 2013, Magnetic resonance in medicine.

[63]  Chun-Hung Yeh,et al.  Diffusion orientation transform revisited , 2010, NeuroImage.

[64]  Andrew L. Alexander,et al.  Hybrid diffusion imaging , 2007, NeuroImage.

[65]  Hans-Peter Meinzer,et al.  Opportunities and pitfalls in the quantification of fiber integrity: What can we gain from Q-ball imaging? , 2010, NeuroImage.

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

[67]  Lester Melie-García,et al.  Deconvolution in diffusion spectrum imaging , 2010, NeuroImage.

[68]  Wolfhard Semmler,et al.  Advanced fit of the diffusion kurtosis tensor by directional weighting and regularization , 2012, Magnetic resonance in medicine.

[69]  Yaniv Gur,et al.  White matter structure assessment from reduced HARDI data using low-rank polynomial approximations. , 2012, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[70]  Bengt Jönsson,et al.  Restricted Diffusion in Cylindrical Geometry , 1995 .

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

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

[73]  P. Basser.  Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy , 2009 .