AUTHOR=Narvaez Omar , Svenningsson Leo , Yon Maxime , Sierra Alejandra , Topgaard Daniel 

TITLE=Massively Multidimensional Diffusion-Relaxation Correlation MRI

JOURNAL=Frontiers in Physics

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.793966

DOI=10.3389/fphy.2021.793966

ISSN=2296-424X

ABSTRACT=<p>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 <bold>b</bold>(<italic>ω</italic>) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “<bold>D</bold>(<italic>ω</italic>)-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, <italic>R</italic><sub>1</sub> and <italic>R</italic><sub>2,</sub> with (<italic>ω</italic>-independent) <bold>D</bold>-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 <italic>ex vivo</italic> 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 <bold>b</bold>(<italic>ω</italic>), as well as the repetition time and echo time, yield nonparametric <bold>D</bold>(<italic>ω</italic>)-<italic>R</italic><sub>1</sub>-<italic>R</italic><sub>2</sub>-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.</p>