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Abstract #2437

Quantification of multiple diffusion metrics from asymmetric balanced SSFP frequency profiles using neural networks

Florian Birk1, Felix Glang1, Christoph Birkl2, Klaus Scheffler1,3, and Rahel Heule1,3
1High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria, 3Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany

Asymmetries in the balanced SSFP frequency profile are known to reflect information about intravoxel tissue microenvironment with strong sensitivity to white matter fiber tract orientation. Phase-cycled bSSFP has demonstrated potential for multi-parametric quantification of relaxation times, static and transmit field inhomogeneity, or conductivity, but has not yet been investigated for diffusion quantification. Therefore, a neural network approach is suggested, which learns a model for voxelwise quantification of diffusion metrics from bSSFP profiles. Not only the feasibility for robust predictions of mean diffusivity (MD) and fractional anisotropy (FA) is shown, but also potential to estimate the principal diffusion eigenvector.

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