Modeling white matter microstructure with fiber ball imaging

ABSTRACT Fiber ball imaging (FBI) provides a means of calculating the fiber orientation density function (fODF) in white matter from diffusion MRI (dMRI) data obtained over a spherical shell with a b‐value of about 4000s/mm2 or higher. By supplementing this FBI‐derived fODF with dMRI data acquired for two lower b‐value shells, it is shown that several microstructural parameters may be estimated, including the axonal water fraction (AWF) and the intrinsic intra‐axonal diffusivity. This fiber ball white matter (FBWM) modeling method is demonstrated for dMRI data acquired from healthy volunteers, and the results are compared with those of the white matter tract integrity (WMTI) method. Both the AWF and the intra‐axonal diffusivity obtained with FBWM are found to be significantly larger than for WMTI, with the FBWM values for the intra‐axonal diffusivity being more consistent with recent results obtained using isotropic diffusion weighting. An important practical advantage of FBWM is that the only nonlinear fitting required is the minimization of a cost function with just a single free parameter, which facilitates the implementation of efficient and robust numerical routines. HIGHLIGHTSA method of estimating microstructural parameters for white matter is proposed.The method is based on the diffusion MRI technique known as fiber ball imaging.The main numerical step is minimizing a cost function with a single free parameter.The method is easily implemented, and the parameter estimates are reproducible.The method improves upon the white matter tract integrity approach.

[1]  D. A. Dunnett Classical Electrodynamics , 2020, Nature.

[2]  Joseph A. Helpern,et al.  Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain. , 2017, Magnetic resonance imaging.

[3]  Leif Østergaard,et al.  Modeling dendrite density from magnetic resonance diffusion measurements , 2007, NeuroImage.

[4]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

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

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

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

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

[9]  J. Veraart,et al.  Universal power-law scaling of water diffusion in human brain defines what we see with MRI , 2016, 1609.09145.

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

[11]  Joseph A. Helpern,et al.  Fiber ball imaging , 2016, NeuroImage.

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

[13]  D. Yablonskiy,et al.  On the nature of the NAA diffusion attenuated MR signal in the central nervous system , 2004, Magnetic resonance in medicine.

[14]  J. Helpern,et al.  MRI quantification of non‐Gaussian water diffusion by kurtosis analysis , 2010, NMR in biomedicine.

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

[16]  Jens H Jensen,et al.  Evaluating kurtosis‐based diffusion MRI tissue models for white matter with fiber ball imaging , 2017, NMR in biomedicine.

[17]  Hui Zhang,et al.  Imaging brain microstructure with diffusion MRI: practicality and applications , 2019, NMR in biomedicine.

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

[19]  Bibek Dhital,et al.  Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.

[20]  Daniel C. Alexander,et al.  Multi-compartment microscopic diffusion imaging , 2016, NeuroImage.

[21]  A. MacKay,et al.  Magnetic Resonance of Myelin Water: An in vivo Marker for Myelin , 2016, Brain plasticity.

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

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

[24]  Daniel C. Alexander,et al.  Ranking diffusion-MRI models with in-vivo human brain data , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[25]  D. Le Bihan,et al.  Water diffusion compartmentation and anisotropy at high b values in the human brain , 2000, Magnetic resonance in medicine.

[26]  Derek K. Jones,et al.  Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .

[27]  C. Wheeler-Kingshott,et al.  A ranking of diffusion MRI compartment models with in vivo human brain data , 2013, Magnetic resonance in medicine.

[28]  Dmitriy A Yablonskiy,et al.  Theoretical models of the diffusion weighted MR signal , 2010, NMR in biomedicine.

[29]  Jens H Jensen,et al.  Quantitative assessment of diffusional kurtosis anisotropy , 2015, NMR in biomedicine.

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

[31]  Denis Le Bihan,et al.  Diffusion Magnetic Resonance Imaging: What Water Tells Us about Biological Tissues , 2015, PLoS biology.

[32]  Daniel Topgaard,et al.  Multidimensional diffusion MRI. , 2017, Journal of magnetic resonance.

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

[34]  Rainer Goebel,et al.  Robust and fast nonlinear optimization of diffusion MRI microstructure models , 2017, NeuroImage.

[35]  B. Ardekani,et al.  Estimation of tensors and tensor‐derived measures in diffusional kurtosis imaging , 2011, Magnetic resonance in medicine.

[36]  M. Mallar Chakravarty,et al.  Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy , 2010, NeuroImage.

[37]  Matthew D. Budde,et al.  Design and Validation of Diffusion MRI Models of White Matter , 2017, Front. Phys..

[38]  V. Kiselev,et al.  Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation , 2016, NMR in biomedicine.

[39]  Bibek Dhital,et al.  Intra-axonal diffusivity in brain white matter , 2017, NeuroImage.

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

[41]  M. F. Falangola,et al.  Effect of cerebral spinal fluid suppression for diffusional kurtosis imaging , 2013, Journal of magnetic resonance imaging : JMRI.

[42]  M. Holz,et al.  Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements , 2000 .

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

[44]  Jelle Veraart,et al.  In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy , 2016, NeuroImage.

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

[46]  Yoram Cohen,et al.  From single‐pulsed field gradient to double‐pulsed field gradient MR: gleaning new microstructural information and developing new forms of contrast in MRI , 2010, NMR in biomedicine.

[47]  Jens H Jensen,et al.  Optimization of white matter fiber tractography with diffusional kurtosis imaging , 2015, NMR in biomedicine.

[48]  L. Mason,et al.  COMPLEX ANALYSIS AND THE FUNK TRANSFORM , 2003 .

[49]  Carl-Fredrik Westin,et al.  Q-space trajectory imaging for multidimensional diffusion MRI of the human brain , 2016, NeuroImage.

[50]  Su-Deuk Kim,et al.  Temperature Dependent Self-Diffusion Coefficients of Valinomycin and the Potassium-Valinomycin Complex , 2008 .

[51]  J. Sijbers,et al.  More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging , 2011, Magnetic resonance in medicine.

[52]  F. Ståhlberg,et al.  Noninvasive mapping of water diffusional exchange in the human brain using filter‐exchange imaging , 2013, Magnetic resonance in medicine.

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