In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the 'constrained diffusional variance decomposition' (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.

In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic (“stick‐like”) diffusion. Second, the “density” of tissue components may be confounded by non‐diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with “b‐tensor encoding” and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b‐tensor data is associated with myelinated axons but not with dendrites. Furthermore, b‐tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data‐driven estimates of compartment‐specific T2 values. Finally, the “stick” fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment‐specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained “index” parameters could be valid within limited domains that should be delineated by future studies.

Purpose: Filter exchange imaging (FEXI) is sensitive to the rate of diffusional water exchange, which depends, eg, on the cell membrane permeability. The aim was to optimize and analyze the ability of FEXI to infer differences in the apparent exchange rate (AXR) in the brain between two populations.Methods: A FEXI protocol was optimized for minimal measurement variance in the AXR. The AXR variance was investigated by test‐retest acquisitions in six brain regions in 18 healthy volunteers. Preoperative FEXI data and postoperative microphotos were obtained in six meningiomas and five astrocytomas.Results: Protocol optimization reduced the coefficient of variation of AXR by approximately 40%. Test‐retest AXR values were heterogeneous across normal brain regions, from 0.3 ± 0.2 s⁻¹ in the corpus callosum to 1.8 ± 0.3 s⁻¹ in the frontal white matter. According to analysis of statistical power, in all brain regions except one, group differences of 0.3–0.5 s⁻¹ in the AXR can be inferred using 5 to 10 subjects per group. An AXR difference of this magnitude was observed between meningiomas (0.6 ± 0.1 s⁻¹) and astrocytomas (1.0 ± 0.3 s⁻¹).Conclusions: With the optimized protocol, FEXI has the ability to infer relevant differences in the AXR between two populations for small group sizes. Magn Reson Med 77:1104–1114, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Purpose: To optimize diffusion‐relaxation MRI with tensor‐valued diffusion encoding for precise estimation of compartment‐specific fractions, diffusivities, and T₂ values within a two‐compartment model of white matter, and to explore the approach in vivo.Methods: Sampling protocols featuring different b‐values (b), b‐tensor shapes (b_Δ), and echo times (TE) were optimized using Cramér‐Rao lower bounds (CRLB). Whole‐brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T₂ values were estimated in a model featuring two microstructural compartments represented by a “stick” and a “zeppelin.”Results: Precise parameter estimates were enabled by sampling protocols featuring seven or more “shells” with unique b/b_Δ/TE‐combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the “stick” compartment had a fraction of approximately 0.5 and, compared with the “zeppelin” compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm²/ms) but higher T₂ values (85 vs. 65 ms). Children featured lower “stick” fractions (0.4). White matter lesions exhibited high “zeppelin” isotropic diffusivities (1.7 μm²/ms) and T₂ values (150 ms).Conclusions: Diffusion‐relaxation MRI with tensor‐valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T₂ relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

Objective: Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug‐resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T₁‐ and T₂‐weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor‐valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor‐valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD.Methods: Tensor‐valued dMRI was performed on a 7 T MRI scanner in 13 MCD patients (age = 32 ± 13 years) featuring periventricular heterotopia, subcortical heterotopia, focal cortical dysplasia, and polymicrogyria. Data analysis yielded maps of microscopic anisotropy that were compared with T₁‐weighted and T₂‐fluid‐attenuated inversion recovery images and with the fractional anisotropy from diffusion tensor imaging.Results: Maps of microscopic anisotropy revealed large white matter‐like regions within MCD that were uniformly cortex‐like in the conventional MRI contrasts. These regions were seen particularly in the deep white matter parts of subcortical heterotopias and near the gray‐white boundaries of focal cortical dysplasias and polymicrogyrias.Significance: By being sensitive to axons rather than myelin, mapping of microscopic anisotropy may yield a more robust differentiation of cortex and white matter and improve MCD delineation in presurgical evaluation of epilepsy.