Abstract #1854

In Vivo Evaluation of a Novel Deep Learning-based MR Image Reconstruction for Liver Fat Quantification

Nikolaos Panagiotopoulos^1,2, Nathan T Roberts³, David Harris¹, Daiki Tamada¹, Ty Cashen³, Thekla H Oechtering^1,2, Diego Hernando^1,4,5,6, Claude B Sirlin⁷, and Scott B Reeder^1,4,5,8,9

¹Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany, ³GE Healthcare, Waukesha, WI, United States, ⁴Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Department of Departments of Electrical & Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁷Department of Radiology, University of California San Diego, San Diego, CA, United States, ⁸Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁹Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Liver, Quantitative Imaging

Deep learning (DL)-based MR reconstruction methods show promise to reduce image noise compared to conventional image reconstruction while maintaining quantitative accuracy. The purpose of this work is to apply and evaluate the performance of DL reconstruction to chemical shift-encoded MRI for quantification of proton density fat-fraction (PDFF) in the liver. We compared quantitative PDFF results, test-retest repeatability, and standard deviation within regions of interest in nine patients with a wide range of PDFF (1-31%), for different levels of DL denoising. PDFF between reconstructions showed excellent agreement and constant test-retest repeatability. Standard deviation decreased with increasing DL denoising levels.

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