Chapter 13. Advanced Histology for Validation of Advanced Diffusion MRI

Sierra, A

doi:10.1039/9781788019910-00377

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“…As diffusion-relaxation correlation [52], oscillating gradients [105,106], and tensorvalued encoding [107] with spin echo-prepared EPI read-out are all increasingly used for in vivo human studies on high-end conventional clinical scanners, our massively multidimensional acquisition and analysis approach has potential for translation to clinical research studies using only moderately longer scanning times than each of the individual lowerdimensional methods. Further studies are needed where all the quantitative information from the obtained distributions and maps is integrated with phantoms validation [108], as well as with advanced histological validation [109] of healthy and pathological tissues.…”

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confidence: 99%

Massively Multidimensional Diffusion-Relaxation Correlation MRI

et al. 2022

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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(ω) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “D(ω)-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, R1 and R2, with (ω-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(ω), as well as the repetition time and echo time, yield nonparametric D(ω)-R1-R2-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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