Conventional QSM reconstruction algorithms impose long computation time, which inhibits their adoption for real-time clinical use. In this work, we propose a method that replaces conventional iterative algorithms for background removal and dipole inversion with two deep neural networks. The reconstruction results demonstrate comparable performance to the previous outcomes while the new method takes only 3 seconds (up to 106 times faster!), which is unparalleled to conventional methods.
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