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Quantification of multiple diffusion metrics from asymmetric balanced SSFP frequency profiles using neural networks
{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.}
@misc{item_3319704, title = {{Quantification of multiple diffusion metrics from asymmetric balanced SSFP frequency profiles using neural networks}}, booktitle = {{2021 ISMRM \& SMRT Annual Meeting \& Exhibition (ISMRM 2021)}}, abstract = {{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.}}, year = {2021}, slug = {item_3319704}, author = {Birk, F and Glang, F and Birkl, C and Scheffler, K and Heule, R} }