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Abstract #0514

Deep Learning-based Quantification of Proton Density Fat Fraction and R2* in the Liver

Ante Zhu1,2, Yuxin Zhang2,3, Alan McMillan2, Fang Liu2, Timothy J Colgan2, Scott B. Reeder1,2,3,4,5, and Diego Hernando1,2,3,6
1Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 4Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States, 5Medicine, University of Wisconsin-Madison, Madison, WI, United States, 6Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States

Multi-echo chemical shift-encoded (CSE)-MRI techniques enable liver PDFF and R2* quantification, which enable staging and treatment monitoring of liver fat and iron content, respectively. However, the common requirement of breath-holding in CSE-MRI acquisitions is challenging for many patients. Furthermore, the required specialized multi-echo acquisition and reconstruction are not available in all scanners. In this work, we assessed the accuracy of deep learning (DL)-based PDFF and R2* quantification using reduced numbers of echoes. Preliminary results demonstrate the potential of this approach and suggest that at least four echoes are needed for quantifying PDFF and R2* at 1.5T and 3.0T.

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