Correlation Tensor Magnetic Resonance Imaging
Rafael Neto Henriques, Sune N{\o}rh{\o}j Jespersen, Noam Shemesh

TL;DR
This paper introduces Correlation Tensor MRI (CTI), a novel imaging framework that explicitly decouples isotropic and anisotropic kurtosis sources in diffusion MRI, providing more specific microstructural insights.
Contribution
The study develops a general CTI framework using double diffusion encoding to distinguish kurtosis sources, validated through ex vivo and in vivo experiments in mouse and rat brains.
Findings
CTI can decouple microscopic anisotropy from partial volume effects.
Intra-compartmental kurtosis index shows positive values in brain tissues.
Current protocol limited by higher-order effects not yet modeled.
Abstract
Diffusional Kurtosis Imaging (DKI) is a sensitive biomarker for microstructure in health and disease. However, DKI is not specific to any microstructural property since it may emerge from several different sources. Q-space trajectory encoding has been proposed for decoupling isotropic from anisotropic kurtosis. Still, this method assume that the system is comprised of multiple Gaussian diffusion components. Here, we develop a more general framework for resolving the underlying kurtosis sources. We introduce Correlation Tensor MRI (CTI) - an approach harnessing the versatility of double diffusion encoding (DDE) and capable of explicitly decoupling isotropic and anisotropic kurtosis components from intra-compartmental kurtosis effects arising from restricted diffusion. Additionally, CTI provides an index that is potentially sensitive to intra-compartmental kurtosis. The theoretical…
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