4.6 Article

Massively Multidimensional Diffusion-Relaxation Correlation MRI

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FRONTIERS IN PHYSICS
卷 9, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fphy.2021.793966

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diffusion-relaxation; diffusion tensor distribution; tensor-valued encoding spectrum; rat brain; multidimensional diffusion

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Diverse approaches have been used to study microstructure and heterogeneity in biological tissues. Recent research has enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors and relaxation properties using acquisition schemes with free gradient waveforms. These methods allow investigation of the subtle differences in diffusion and relaxation properties.
Diverse approaches such as oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation have been used to study microstructure and heterogeneity in healthy and pathological biological tissues. Recently, acquisition schemes with free gradient waveforms exploring both the frequency-dependent and tensorial aspects of the encoding spectrum b(omega) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These D(omega)-distributions allow investigation of restricted diffusion for each distinct component resolved in the diffusion tensor trace, anisotropy, and orientation dimensions. Likewise, multidimensional methods combining longitudinal and transverse relaxation rates, R-1 and R-2,R- with (omega-independent) D-distributions capitalize on the component resolution offered by the diffusion dimensions to investigate subtle differences in relaxation properties of sub-voxel water populations in the living human brain, for instance nerve fiber bundles with different orientations. By measurements on an ex vivo rat brain, we here demonstrate a massively multidimensional diffusion-relaxation correlation protocol joining all the approaches mentioned above. Images acquired as a function of the magnitude, normalized anisotropy, orientation, and frequency content of b(omega), as well as the repetition time and echo time, yield nonparametric D(omega)-R-1-R-2-distributions via a Monte Carlo data inversion algorithm. The obtained per-voxel distributions are converted to parameter maps commonly associated with conventional lower-dimensional methods as well as unique statistical descriptors reporting on the correlations between restriction, anisotropy, and relaxation.

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