Improved multi‐echo gradient echo myelin water fraction mapping using complex‐valued neural network analysis

• Published in: Magnetic resonance in medicine Vol. 88; no. 1; pp. 492 - 500
• Main Authors: Jung, Soozy, Yun, JiSu, Kim, Deog Young, Kim, Dong‐Hyun
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2022
• Subjects: artificial neural network; Artificial neural networks; Bias; Brain - diagnostic imaging; Confidence intervals; Estimates; Fiber orientation; In vivo methods and tests; Magnetic Resonance Imaging - methods; Mapping; multi‐echo gradient echo; Myelin; Myelin Sheath; myelin water fraction; Network analysis; Neural networks; Neural Networks, Computer; Substantia alba; Uncertainty; Water; White Matter; Workflow; artificial neural network; myelin water fraction; multi-echo gradient echo; uncertainty
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.29192

Purpose Previously, an artificial neural network method was introduced to estimate quantitative myelin water fraction (MWF) using multi‐echo gradient‐echo data. However, the fiber orientation of white matter with respect to B0 could bias the quantification of MWF. Here, we developed an advanced workflow for MWF estimation that could improve the quantification of MWF. Methods To adopt fiber orientation effects, a complex‐valued neural network with complex‐valued operation was 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 inference was 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 proposed method 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.