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Abstract #1811

Addressing Bias and Precision in Low SNR Chemical Shift Encoded MRI Proton Density Fat Fraction Estimation using a Deep Learning Reconstruction

Nathan T Roberts, PhD1, Nikolaos Panagiotopoulos, MD2, Ty Cashen, MD, PhD1, Daiki Tamada, PhD2, Diego Hernando, PhD2,3,4,5, Arnaud Guidon, PhD1, and Scott B Reeder, MD, PhD2,3,4,6,7
1GE HealthCare, Waukesha, WI, United States, 2Radiology, University of Wisconsin - Madison, Madison, WI, United States, 3Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 4Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States, 5Electrical and Computer 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

Synopsis

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

Low bias and high precision are important for accurate diagnosis, staging, and treatment monitoring of chronic liver disease using chemical shift-encoded (CSE)-MRI. However, CSE-MRI proton density fat fraction (PDFF) measurements are often biased by an asymmetric noise distribution present in PDFF maps acquired with low/moderate signal-to-noise ratio (SNR). This work investigates the use of deep learning de-noising to mitigate this bias in phantoms and in vivo. Results demonstrate that deep learning reconstruction removes or reduces noise-related PDFF estimation bias while maintaining the expected noise distribution characteristic of PDFF.

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Keywords