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Abstract #0254

Effect of combining linear with spherical tensor encoding on estimating brain microstructural parameters

Els Fieremans1, Jelle Veraart1, Benjamin Ades-Aron1, Filip Szczepankiewicz2,3, Markus Nilsson2, and Dmitry S Novikov1

1Radiology, New York University School of Medicine, New York, NY, United States, 2Clinical Sciences, Lund University, Lund, Sweden, 3Random Walk Imaging AB, Lund, Sweden

The diffusion MRI signal, as measured with conventional linear tensor encoding (LTE), has been shown to have not enough features to fully model the white matter microstructure. Here we investigate whether adding spherical encoding (STE) to LTE makes microstructural parameter estimation more robust. On signal simulations and in in vivo MRI data, we demonstrate that the intra-axonal diffusivity and axonal water fraction are estimated with higher precision, thereby enabling a 20 minute whole brain protocol to extract brain microstructural parameters without imposing constraints or priors.

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