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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:BackgroundMulti-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times.PurposeTo investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions.Study typeProspective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b).SubjectsOne normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b).Field strength/sequence3T multi-shell acquisition and 1.5T multi-b acquisition.AssessmentThe "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity.Statistical testsParametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue.ResultsIn the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps.Data conclusionModel-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics.Evidence level3 TECHNICAL EFFICACY STAGE: 1.

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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.

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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