Abstract #3996

Quantitative Susceptibility Mapping of Liver Iron Overload using Deep Learning

Ruiyang Zhao^1,2, Collin J Buelo², Julia V Velikina¹, Steffen Bollmann³, Ante Zhu⁴, Scott B Reeder^1,2,5,6,7, and Diego Hernando^1,2

¹Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia, ⁴GE Global Research, Niskayuna, NY, United States, ⁵Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁷Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

A novel deep learning-based technique for quantitative susceptibility mapping (QSM) of liver iron overload was developed and validated. The proposed method relies on a 3D fully convolutional neural network, trained using synthetic dataset from a digital torso phantom that includes major organs. This study also included patients with iron overload who were imaged under 3T with using a single breath-hold multi-echo acquisition. Results showed promising performance and agreement with reference susceptibility measurements across a wide range of iron overload cases.

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