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Abstract #0054

Deep learning improves accuracy of proton-density fat fraction estimation from In-phase and out-of-phase T1-weighted MRI

Kang Wang1, Guilherme Mourna Cunha2, Kyle Hasenstab3, Walter C Henderson4, Michael S Middleton4, Rohit Loomba5, Shelley A Cole6, Albert Hsiao4, and Claude B Sirlin7
1Radiology, Stanford, Palo Alto, CA, United States, 2Radiology, University of Washington Medicine, Seattle, WA, United States, 3Department of Mathematics and Statistics, San Diego State University, San Diego, CA, United States, 4Radiology, UC San Diego, La Jolla, CA, United States, 5Department of Hepatology, UC San Diego, La Jolla, CA, United States, 6Texas Biomedical Research Institute, San Antonio, TX, United States, 7UC San Diego, La Jolla, CA, United States

Synopsis

Keywords: Liver, Quantitative Imaging, Fat, Data analysisProton density fat fraction (PDFF) is an established quantitative-imaging-biomarker for hepatic -fat quantification, but typically requires specialized confounder-corrected chemical-shift-encoded (CSE) magnetic-resonance-imaging (MRI) pulse sequences. We developed and assessed the feasibility of deep learning to infer hepatic PDFF maps from conventional T1-weighted-in-and-opposed-phase (T1w-IOP) MRI. Using PDFF maps reconstructed from CSE-MRI as reference, we trained a convolutional-neural-network (CNN) to infer voxel-wise PDFF maps from T1w-IOP MRI. The CNN was evaluated using both internal and external test datasets. Participant-level median CNN-inferred-PDFF were compared with reference CSE-MRI using linear regression, intraclass correlation, and Bland-Altman analysis. Median CNN-inferred PDFF agreed closely with reference CSE-MRI PDFF.

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Keywords