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Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding.

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 (b? ), 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? /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 ?m2 /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 ?m2 /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.

SUBMITTER: Lampinen B

PROVIDER: S-EPMC7652211 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding.

Lampinen Björn B Szczepankiewicz Filip F Mårtensson Johan J van Westen Danielle D Hansson Oskar O Westin Carl-Fredrik CF Nilsson Markus M

Magnetic resonance in medicine 20200306 3

<h4>Purpose</h4>To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T<sub>2</sub> values within a two-compartment model of white matter, and to explore the approach in vivo.<h4>Methods</h4>Sampling protocols featuring different b-values (b), b-tensor shapes (b<sub>Δ</sub> ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, ...[more]

PMID: 32141131

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Project description:Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/?m2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.

Project description:BackgroundThe progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease.MethodsParticipants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FATissue) and mean diffusivity (MDTissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors.ResultsIn the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants.DiscussionBaseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.

Project description:Background It is well known that white matter injuries observed at birth are associated with adverse neurodevelopmental outcomes later in life. Whether white matter developmental variations in healthy newborns are also associated with changes in later neurodevelopment remains to be established. Purpose To evaluate whether developmental variations of white matter microstructures identified by MRI correlate with neurodevelopmental outcomes in healthy full-term infants. Materials and Methods In this prospective study, pregnant women were recruited and their healthy full-term newborns underwent a brain MRI including diffusion tensor imaging at approximately 2 weeks of age. These infants were tested at approximately 2 years of age with the Bayley Scales of Infant Development (BSID). Voxel-wise correlation analyses of fractional anisotropy (FA), measured with diffusion tensor MRI, and neurodevelopmental test scores, measured by using BSID, were performed by using tract-based spatial statistics (TBSS), followed by region-of-interest (ROI) analyses of correlations between mean FA in selected white matter ROIs and each BSID subscale score. Results Thirty-eight full-term infants (20 boys, 18 girls) underwent MRI examination at 2 weeks of age (14.3 days ± 1.6) and BSID measurement at 2 years of age (732 days ± 6). TBSS analyses showed widespread clusters in major white matter tracts, with positive correlations (P ≤ .05, corrected for the voxel-wise multiple comparisons) between FA values and multiple BSID subscale scores. These correlations were largely independent of several demographic parameters as well as family environment. Gestational age at birth appeared to be a confounding factor as TBSS-observed correlations weakened when it was included as a covariate; however, after controlling for gestational age at birth, ROI analyses still showed positive correlations (P ≤ .05, R = 0.35 to 0.48) between mean FA in many white matter ROIs and BSID cognitive, language, and motor scores. Conclusion There were significant associations between white matter microstructure developmental variations in healthy full-term newborns and their neurodevelopmental outcomes. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Hu and McAllister in this issue.

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Dataset Information

Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding.

Publications

Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding.

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