| 1. |
- Lampinen, Björn, et al.
(författare)
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Searching for the neurite density with diffusion MRI : Challenges for biophysical modeling
- 2019
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 40:8, s. 2529-2545
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Szczepankiewicz, Filip, et al.
(författare)
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Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors
- 2015
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Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 104, s. 241-252
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Tidskriftsartikel (refereegranskat)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 (mu FA) 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 mu FA in various micro-architectures. Generally, mu FA was high in the white matter and low in the gray matter. In the white matter, the largest differences between mu FA and FA were found in crossing white matter and in interfaces between large white matter tracts, where mu FA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the mu FA 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 mu FA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence. (C) 2014 The Authors. Published by Elsevier Inc.
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| 3. |
- Szczepankiewicz, Filip, et al.
(författare)
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The link between diffusion MRI and tumor heterogeneity : Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)
- 2016
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119. ; 142, s. 522-532
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Tidskriftsartikel (refereegranskat)abstract
- The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKA correlated with cell eccentricity (r = 0.95, p < 10− 7) and MKI with the cell density variance (r = 0.83, p < 10− 3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r = 0.80, p < 10− 3) and microscopic scale (μFA, r = 0.93, p < 10− 6). A multiple regression analysis showed that the conventional MKT parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKA was associated only to cell eccentricity, and MKI only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean ± s.d.) MKA = 1.11 ± 0.33 vs MKI = 0.44 ± 0.20 (p < 10− 3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI = 0.57 ± 0.30 vs MKA = 0.26 ± 0.11 (p < 0.05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.
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