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Abstract #4029

Achieving Real-Time QSM Reconstruction Using Deep Neural Network

Hoeseong Kim1,2, Jaeyeon Yoon1,2, and Jongho Lee1

1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2AIRS medical, Seoul, Korea, Republic of

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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