Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

Abstract

The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for “tissue integrity”, especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.

Axon Diameters and Myelin Content Modulate Microscopic Fractional Anisotropy at Short Diffusion Times in Fixed Rat Spinal Cord

“…Methods other than D(O)DE, targeting µFA such as tailoring b-tensor shapes are emerging, with many potential applications [77][78][79][80]. However, such methods may be confounded by time-dependent diffusion effects [27,[81][82][83], whereas D(O)DE at long mixing times naturally avoids these confounds [43].…”

NMR diffusion-encoding with axial symmetry and variable anisotropy: Distinguishing between prolate and oblate microscopic diffusion tensors with unknown orientation distribution

“…17,19,53 In favorable cases, prolate and oblate diffusion tensors give visibly different signals already at attenuation levels S/S 0 around 0.2, indicating that the signal-to-noise ratios observed in vivo with analogous pulse sequences 19 could be sufficient for successful clinical implementation of the method. As for a clinical application, our method could differentiate tumors by their average cell shape, either prolate or oblate, which is today not possible if the tumor cells are randomly oriented, and the diffusion is macroscopically isotropic.…”

“…17,19 In common with other approaches for quantifying microscopic anisotropy, 20,21 our previous version relies on a single rotationally invariant metric that does not distinguish between prolate and oblate shapes of the underlying microscopic diffusion tensors. Since the tensor shape is directly related to the geometry of the compartments in which the studied pore liquid resides, measurements of the shape could be useful for distinguishing between planar and cylindrical pore geometries in materials ranging from liquid crystals 3 and porous solids 22,23 to brain tumors.…”

Mild White Matter Changes in Un-Medicated Obsessive-Compulsive Disorder Patients and Their Unaffected Siblings

“…We suggest several possible reasons for the reported discrepancies: (1) in OCD, white matter alterations might be subtle thus difficult to detect; (2) medication effects seem to confound the findings (Yoo et al, 2007;Fan et al, 2012;Benedetti et al, 2013;Radua et al, 2014); (3) results are highly variable across pediatric, adolescent and adult patients due to changes in white matter architecture throughout brain development (Peters et al, 2012); (4) debate has risen recently on whether FA alone is sufficient and sufficiently representative to indicate changes in white matter microstructure (Hasan, 2006;Fan et al, 2012;Szczepankiewicz et al, 2014); and (5) small samples and inconsistent methodologies have been used (Radua et al, 2014).…”