A unique analytical solution of the white matter standard model using linear and planar encodings

It is known that white matter modeling based on commonly used linear diffusion encoding is an ill‐posed problem. We analyze the additional information gained from a double pulsed diffusion encoding.

[1]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[2]  Joseph A. Helpern,et al.  White matter characterization with diffusional kurtosis imaging , 2011, NeuroImage.

[3]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[4]  Sune Nørhøj Jespersen,et al.  Equivalence of double and single wave vector diffusion contrast at low diffusion weighting , 2012, NMR in biomedicine.

[5]  C. Sønderby,et al.  Orientationally invariant metrics of apparent compartment eccentricity from double pulsed field gradient diffusion experiments , 2013, NMR in biomedicine.

[6]  Carl-Fredrik Westin,et al.  Measurement Tensors in Diffusion MRI: Generalizing the Concept of Diffusion Encoding , 2014, MICCAI.

[7]  J. Jensen,et al.  Sufficiency of diffusion tensor in characterizing the diffusion MRI signal to leading order in diffusion weighting , 2014, NMR in biomedicine.

[8]  Carl-Fredrik Westin,et al.  Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors , 2015, NeuroImage.

[9]  J. Veraart,et al.  Mapping orientational and microstructural metrics of neuronal integrity with in vivo diffusion MRI , 2016, 1609.09144.

[10]  J. Veraart,et al.  Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue , 2016, NMR in biomedicine.

[11]  Markus Nilsson,et al.  Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding , 2017, NeuroImage.

[12]  Brian Hansen,et al.  Precision and accuracy of diffusion kurtosis estimation and the influence of b‐value selection , 2017, NMR in biomedicine.

[13]  Jürgen Hennig,et al.  Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach , 2017, NeuroImage.

[14]  Jelle Veraart,et al.  TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T 2 relaxation times , 2017, NeuroImage.

[15]  Jelle Veraart,et al.  Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI , 2018, NeuroImage.

[16]  Alejandro F. Frangi,et al.  Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI , 2018, 1809.05059.

[17]  Brian Hansen,et al.  Diffusion time dependence of microstructural parameters in fixed spinal cord , 2017, NeuroImage.

[18]  Bibek Dhital,et al.  The absence of restricted water pool in brain white matter , 2017, NeuroImage.

[19]  Nicolas Kunz,et al.  Intra- and extra-axonal axial diffusivities in the white matter: Which one is faster? , 2018, NeuroImage.

[20]  Alejandro F. Frangi,et al.  Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding , 2018, Magnetic resonance in medicine.

[21]  Jelle Veraart,et al.  On the scaling behavior of water diffusion in human brain white matter , 2019, NeuroImage.