| 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. |
- Lampinen, Björn, et al.
(författare)
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Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding
- 2020
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Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 0740-3194 .- 1522-2594. ; 84:3, s. 1605-1623
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Tidskriftsartikel (refereegranskat)abstract
- 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 (bΔ), 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/bΔ/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.
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| 3. |
- Nilsson, Markus, et al.
(författare)
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Tensor-valued diffusion MRI in under 3 minutes : an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors
- 2020
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Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 0740-3194 .- 1522-2594. ; 83:2, s. 608-620
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors. Methods: B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MKA) and isotropic kurtosis (MKI), respectively. An extensive imaging protocol was compared with a 3-minutes protocol. Results: The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MKA = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MKI = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03). Conclusion: Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- Westin, Carl-Fredrik, et al.
(författare)
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Q-space trajectory imaging for multidimensional diffusion MRI of the human brain
- 2016
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 135, s. 345-362
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Tidskriftsartikel (refereegranskat)abstract
- This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.
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