Synopsis

Tensor-valued diffusion encoding enables disentangling isotropic and anisotropic diffusion components. However, its impact on estimating brain microstructural features has only been studied in a handful of parametric models. In this work, we evaluate the Magic DIAMOND model, that allows characterization of crossing fascicles and assessment of diffusivities for each, using combinations of linear, planar and spherical encodings in vivo. Building statistics through stratified bootstrap, we show that spherical encoding substantially increases the variance in estimated parameters and should be avoided. Planar encoding, on the other hand, did not offer clear improvement or worsening within our current acquisition scheme and setup.

Context and purpose

Complementing the single (linear) diffusion encoding yielded by "Stejskal-Tanner"-like sequences¹, double² (planar) and triple³ (spherical) diffusion encodings have recently drawn much attention^4-10, especially with the emergence of tensor-valued diffusion encoding^11-15. From a modeling standpoint, the main interest lies in assessing whether the estimation of microstructural features benefits from non-trivial diffusion encodings or not. The impact of planar and spherical encodings has already been investigated in “ball and stick”-like models such as the white matter Standard Model¹⁶ and NODDI¹⁷, focusing mainly on precision^18,19 and degeneracy^20,21 in parameter estimation. In this work, we study their impact on a multi-fascicle compartment model, the Magic DIAMOND model²², a b-tensor extension of the DIAMOND model^23,24 that allows not only modeling of crossing fascicles but also individual characterization of their diffusional features.

Theory

Within Magic DIAMOND²², the diffusion-weighted signal is modeled in each voxel as the weighted sum over a free water (FW) compartment and up to three anisotropic compartments (fascicles) of the Laplace transforms of each compartment's diffusion tensor distribution (DTD):$$\frac{\mathcal{S}(\mathbf{B})}{\mathcal{S}_0}=f_\text{FW}\,\frac{\mathcal{S}_\text{FW}(b)}{\mathcal{S}_0}+\sum_{j=1}^{N_\text{f}}f_j\,\frac{\mathcal{S}_j(\mathbf{B})}{\mathcal{S}_0}=f_\text{FW}\,\mathrm{exp}(-bD_\text{FW})+\sum_{j=1}^{N_\text{f}}f_j\int_{\mathbf{D}\in\mathrm{Sym}^+(3)}\!\mathcal{P}_{\Gamma,j}(\mathbf{D})\,\mathrm{exp}(-\mathbf{B}:\mathbf{D})\,\mathrm{d}\mathbf{D}\,,$$where $$$\mathcal{S}_0=\mathcal{S}(\mathbf{B}=\mathbf{0})$$$, the $$$f$$$'s are the different signal fractions, $$$N_\text{f}\in\{0,1,2,3\}$$$ is the number of intra-voxel fascicles, $$$\mathrm{Sym}^+(3)$$$ is the set of $$$3\times 3$$$ symmetric positive-definite matrices, $$$\mathbf{B}$$$ is the b-tensor associated to the q-space trajectory, $$$b=\mathrm{Tr}(\mathbf{B})$$$ is the usual b-value, and $$$\mathbf{B}:\mathbf{D}=\sum_{ik}B_{ik}\,D_{ik}$$$ is the Frobenius inner product. Model selection based on the Akaike criterion is performed to set $$$N_\text{f}$$$ in each voxel. While the free water diffusion is treated as a ball of fixed diffusivity $$$D_\text{FW}=3\;\mu\mathrm{m^2/ms}$$$, each fascicle $$$j$$$ is characterized by a 6D non-central matrix-variate Gamma distribution^22-27 $$$\mathcal{P}_{\Gamma,j}(\mathbf{D})$$$.

Let us omit the compartment index $$$j$$$ for clarity. In the axisymmetric diffusion case, the average compartmental diffusion tensor $$$\mathbf{D}^0$$$ ($$$\mathcal{P}_\Gamma$$$'s expectation) only has two distinct eigenvalues: $$$\lambda^\perp\leq\lambda^\parallel$$$. Considering $$$\mathcal{P}_\Gamma$$$'s shape parameters $$$\kappa^\parallel$$$ and $$$\kappa^\perp$$$ that enable the description of an asymmetric DTD in $$$\mathrm{Sym}^+(3)$$$ and defining $$$\beta$$$ as the Euler angle separating the main axes of $$$\mathbf{B}$$$ and $$$\mathbf{D}^0$$$, Magic DIAMOND yields²² the following signals$$\frac{\mathcal{S}_\text{lin.}}{\mathcal{S}_0}=\left[1+b\left(\frac{\lambda^\parallel}{\kappa^\parallel}\cos^2\beta+\frac{\lambda^\perp}{\kappa^\perp}\sin^2\beta\right)\right]^{-\kappa^\perp}\mathrm{exp}\!\left[\frac{-b\,(\kappa^\parallel-\kappa^\perp)\,\lambda^\parallel\cos^2\beta}{\kappa^\parallel+b\left(\lambda^\parallel\cos^2\beta+\frac{\kappa^\parallel}{\kappa^\perp}\,\lambda^\perp\sin^2\beta \right)}\right]\,,$$

$$\frac{\mathcal{S}_\text{plan.}}{\mathcal{S}_0}=\left(1+\frac{b}{2}\,\frac{\lambda^\perp}{\kappa^\perp}\right)^{-\kappa^\perp}\left[1+\frac{b}{2}\left(\frac{\lambda^\parallel}{\kappa^\parallel}\sin^2\beta+\frac{\lambda^\perp}{\kappa^\perp}\cos^2\beta \right)\right]^{-\kappa^\perp}\mathrm{exp}\!\left[\frac{-b\,(\kappa^\parallel-\kappa^\perp)\,\lambda^\parallel\sin^2\beta}{2\kappa^\parallel+b\left(\lambda^\parallel\sin^2\beta+\frac{\kappa^\parallel}{\kappa^\perp}\,\lambda^\perp\cos^2\beta\right)}\right]\,,$$and$$\frac{\mathcal{S}_\text{sph.}}{\mathcal{S}_0}=\left[\left(1+\frac{b}{3}\,\frac{\lambda^\perp}{\kappa^\perp}\right)^2\left(1+\frac{b}{3}\,\frac{\lambda^\parallel}{\kappa^\parallel}\right)\right]^{-\kappa^\perp}\mathrm{exp}\!\left[\frac{-b\,(\kappa^\parallel-\kappa^\perp)\,\lambda^\parallel}{3\kappa^\parallel+b\,\lambda^\parallel}\right]$$for linear, planar and spherical encodings, respectively.

Acquisitions and methods

MRI acquisitions were performed on a clinical 3T system with 45mT/m maximum gradient amplitude (Ingenia, Philips Healthcare, Best, the Netherlands) using a 32-channel head coil. Imaging was performed on one healthy male volunteer using a prototype diffusion-weighted spin-echo EPI sequence with numerically optimized²⁸ spherical, planar and linear encoding waveforms of similar frequency contents²⁹. Acquisition parameters were: TR=6500ms, TE=121ms, spatial resolution=2.5x2.5x2.5mm³, 48 slices, SENSE factor=1.9, Multiband-SENSE factor=2, multi-shell scheme³⁰ of 45 directions with 1xb=0, 6xb=100, 6xb=700, 12xb=1400 and 20xb=2000 (s/mm²). The acquired images were eddy-corrected and resampled to 2x2x2mm³ using linear interpolation.

We considered three encoding combinations: 45 linear + 45 linear gradients (LL), 45 linear + 45 planar gradients (LP) and 45 linear + 45 spherical gradients (LS). We acquired twice each of these combinations and evaluated them using stratified bootstrap. Stratified bootstrap creates a large number of "virtual" acquisitions by randomly choosing for each diffusion gradient one of the two repetitions, allowing simulation of a large number of noise realizations. Statistics were then computed within a white-matter mask to assess the precision of estimated parameters throughout these realizations.

Results and discussion

Conclusion

Acknowledgements

M. Descoteaux was supported by his NSERC Discovery grant and the NeuroInformatics USherbrooke Institutional Research Chair.

B. Scherrer was supported in part by the National Institutes of Health (NIH) grants R01 NS079788, U01 NS082320 and by Boston Children's Hospital Innovator Award.

We warmly thank Filip Szczepankiewicz and Markus Nilsson for their invaluable help in implementing the q-space trajectories on our clinical scanner.

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