Reproducibility and consistency of evaluation techniques for HARDI data

ObjectThe reproducibility of three evaluation techniques for high angular resolution diffusion imaging (HARDI) data, the diffusion tensor model, q-ball reconstruction and spherical deconvolution, are compared.Materials and methodsTwo healthy volunteers were measured in a 3 T MR system six times with the same measurement parameters; one subject was measured with different b-values. The data was evaluated to compare the consistency and reproducibility of reconstructed diffusion directions and anisotropy values for the three investigated diffusion evaluation techniques. The angle difference between the reconstructed main directions of diffusion for the investigated techniques was evaluated. For q-ball and spherical deconvolution the consistency of the second dominant diffusion direction was additionally examined.ResultsThe differences between the tensor model and q-ball or spherical deconvolution in the estimated diffusion direction decrease with an increase in fractional anisotropy. Increasing the smoothing kernel in q-ball reconstruction renders the results similar to the ones from the diffusion tensor evaluation. Consistency in the reconstructed directions did increase for larger b-values.ConclusionThe evaluation of HARDI data in clinical conditions with q-ball or spherical deconvolution shows consistency and reproducibility similar to the diffusion tensor model, but provides valuable additional information about a second dominant direction of diffusion.

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

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

[3]  Petra S. Huppi,et al.  Combination of event-related fMRI and diffusion tensor imaging in an infant with perinatal stroke , 2004, NeuroImage.

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

[5]  Derek K. Jones,et al.  Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI , 2003, Magnetic resonance in medicine.

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

[7]  Duan Xu,et al.  Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis , 2006, Magnetic resonance in medicine.

[8]  P. Barker,et al.  Diffusion magnetic resonance imaging: Its principle and applications , 1999, The Anatomical record.

[9]  T. Mareci,et al.  Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging , 2003, Magnetic resonance in medicine.

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

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

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

[13]  D. LeBihan,et al.  Validation of q-ball imaging with a diffusion fibre-crossing phantom on a clinical scanner , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[14]  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.

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

[16]  A. Connelly,et al.  Anisotropic noise propagation in diffusion tensor MRI sampling schemes , 2003, Magnetic resonance in medicine.

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

[18]  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.

[19]  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.

[20]  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.

[21]  V. Wedeen,et al.  Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.