Extrapolating fiber crossings from DTI data : can we gain the same information as HARDI?

High angular resolution diffusion imaging (HARDI) has proven to better characterize complex intra-voxel structures compared to its predecessor diffusion tensor imaging (DTI). However, the benefits from the modest acquisition costs and significantly higher signal-to-noise ratios (SNRs) of DTI make it more attractive for use in clinical research. In this work we use contextual information derived from DTI data, to obtain similar crossing information as from HARDI data. We conduct a synthetic phantom study under different angles of crossing and different SNRs. We compare the extrapolated crossings from contextual information with HARDI data. We qualitatively corroborate our findings from the phantom study to real human data. We show that with extrapolation of the contextual information, the obtained crossings are similar to the ones from the HARDI data, and the robustness to noise is significantly better.

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

[2]  M. Descoteaux High angular resolution diffusion MRI : from local estimation to segmentation and tractography , 2008 .

[3]  Bart M. ter Haar Romeny,et al.  Optimal Acquisition Schemes in High Angular Resolution Diffusion Weighted Imaging , 2008, MICCAI.

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

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

[6]  Remco Duits,et al.  Accelerated Diffusion Operators for Enhancing DW-MRI , 2010, VCBM.

[7]  Remco Duits,et al.  Left-invariant diffusions on R^3 x S^2 and their application to crossing-preserving smoothing on HARDI-images , 2009 .

[8]  L. Frank Characterization of anisotropy in high angular resolution diffusion‐weighted MRI , 2002, Magnetic resonance in medicine.

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

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

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

[12]  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.

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

[14]  Baba C. Vemuri,et al.  Extracting Tractosemas from a Displacement Probability Field for Tractography in DW-MRI , 2008, MICCAI.

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

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

[17]  Remco Duits,et al.  Left-Invariant Diffusions on the Space of Positions and Orientations and their Application to Crossing-Preserving Smoothing of HARDI images , 2011, International Journal of Computer Vision.

[18]  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.

[19]  M. Moseley,et al.  Magnetic Resonance in Medicine 51:924–937 (2004) Characterizing Non-Gaussian Diffusion by Using Generalized Diffusion Tensors , 2022 .