Improved multi-echo gradient echomyelin water fraction mapping using complex-valued neural network analysis

Purpose: Previously, an artificial neural network methodwas introduced to estimate quantitativemyelin water fraction (MWF) usingmulti-echo gradient-echo data. However, the fiber orientation of whitematterwith respect to B0 could bias the quantification ofMWF. Here, we developed an advancedworkflow forMWF estimation that could improve the quantification of MWF. Methods: To adopt fiber orientation effects, a complex-valued neural network with complex-valued operationwas used. In addition, to compensate for the bias from different scan parameters, a signal model incorporating the T1 value was devised for training data generation. At the testing stage, a voxel- spread function approach was utilized for spatial B0 artifact correction. Finally, dropout-based variational inferencewas implemented for uncertainty estimates on the network model to provide a confidence interpretation of the output. Results: According to simulation and in vivo analysis, the proposedmethod suggests improved quality of MWF estimation by correcting the bias and artifacts. The proposed complex-valued neural network approach can alleviate the dependency of fiber orientation effects compared to previous artificial neural network method. Uncertainty estimates provides information different from fitting error that can be used as a confidence level of the resulting MWF values. Conclusion: An improved MWF mapping using complex-valued neural network analysis has been proposed.