Abstract #0514

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

Ante Zhu^1,2, Yuxin Zhang^2,3, Alan McMillan², Fang Liu², Timothy J Colgan², Scott B. Reeder^1,2,3,4,5, and Diego Hernando^1,2,3,6

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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