Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions.

[1]  W. Tseng,et al.  Sparse Solution of Fiber Orientation Distribution Function by Diffusion Decomposition , 2013, PloS one.

[2]  Fang-Cheng Yeh,et al.  Estimation of fiber orientation and spin density distribution by diffusion deconvolution , 2011, NeuroImage.

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

[4]  Douglas L. Rosene,et al.  The Geometric Structure of the Brain Fiber Pathways , 2012, Science.

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

[6]  H. Pfeifer Principles of Nuclear Magnetic Resonance Microscopy , 1992 .

[7]  Jorge Jovicich,et al.  Reproducibility and biases in high field brain diffusion MRI: An evaluation of acquisition and analysis variables. , 2013, Magnetic resonance imaging.

[8]  Li-Wei Kuo,et al.  Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system , 2008, NeuroImage.

[9]  I. Koerte,et al.  Diffusion Tensor Imaging , 2014 .

[10]  Timothy D. Verstynen,et al.  Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.

[11]  Heidi Johansen-Berg,et al.  Diffusion MRI at 25: Exploring brain tissue structure and function , 2012, NeuroImage.

[12]  J. Tournier,et al.  High Angular Resolution Diffusion Imaging , 2016 .

[13]  N J Pelc,et al.  Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion‐weighted imaging , 2003, Magnetic resonance in medicine.

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

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

[16]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[17]  J. Fernandez-Miranda,et al.  Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review. , 2014, Biochimica et biophysica acta.

[18]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[19]  Kevin C. Chan,et al.  B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes , 2010, NeuroImage.

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

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

[22]  Bruce R. Rosen,et al.  MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI , 2016, NeuroImage.

[23]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

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

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

[26]  Rachid Deriche,et al.  Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI , 2014, IEEE Transactions on Medical Imaging.

[27]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

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

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

[30]  Julien Cohen-Adad,et al.  The Human Connectome Project and beyond: Initial applications of 300mT/m gradients , 2013, NeuroImage.

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

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

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