Abstract #4919

Global Information Matters in Quantitative Susceptibility Mapping Using 3D Fully Convolutional Neural Networks

Yicheng Chen^1,2, Angela Jakary², Christopher Hess², and Janine Lupo^1,2

¹The UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, CA, United States, ²Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

Recent research has shown that deep convolutional neural networks (DCNNs) have the potential to solve the ill-posed dipole inversion problem in quantitative susceptibility mapping (QSM). This study investigates the effects of patch-based QSM reconstruction by modifying a DCNN to take global susceptibility-phase relation into consideration.

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