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Abstract #3542

Double diffusion encoding enables unique parameter estimation of the Standard Model in diffusion MRI

Santiago Coelho1,2, Jose M. Pozo1,2, Sune N. Jespersen3,4, Derek K. Jones5,6, and Alejandro F. Frangi1,2

1Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, United Kingdom, 2Leeds Institute for Cardiac and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, United Kingdom, 3Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, 4Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark, 5Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, 6School of Psychology, Australian Catholic University, Melbourne, Australia

The widely adopted Standard Model (SM) for diffusion in white matter tissue has been shown to possess intrinsic degeneracies, making parameter estimation from single diffusion encoding (SDE) data ill-conditioned. We extend the SM to a multidimensional diffusion MRI acquisition and analyse the case where the fibre orientation distribution function (fODF) is a Watson distribution. The information contained in the kurtosis tensor, accessed by SDE, is insufficient to recover biophysical model parameters from the MR signal. We prove that Double Diffusion Encoding (DDE), letting us additionally access the full diffusion tensor covariance, makes parameter estimation well-posed.

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