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Abstract #0312

Physics-informed deep neural network for tri-exponential intravoxel incoherent motion fitting in non-alcoholic fatty liver disease.

Marian A. Troelstra1, Julia J. Witjes2, Anne-Marieke van Dijk2, Anne Linde Mak2, Jurgen H. Runge1, Joanne Verheij3, Max Nieuwdorp2, Adriaan G. Holleboom2, Aart J. Nederveen1, and Oliver J. Gurney-Champion1
1Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, Amsterdam, Netherlands, 2Department of Internal and Vascular Medicine, Amsterdam UMC, location AMC, Amsterdam, Netherlands, 3Department of Pathology, Amsterdam UMC, location AMC, Amsterdam, Netherlands

In this study we have developed an unsupervised physics-informed deep neural network (IVIM3-NET) to fit a tri-exponential model to intravoxel incoherent motion (IVIM) data from 35 non-alcoholic fatty liver disease (NAFLD) patients. Diagnostic performance was compared to a tri-exponential least squares (LSQ) fit. Visually, IVIM3-NET showed high-quality parameter maps with less noise than the LSQ-fit. IVIM3-NET showed slightly higher correlations between fit parameters and histology and more significant differences between levels of fibrosis and inflammation than the LSQ-fit. Correlations between f2 and fibrosis and inflammation grade, potentially highlighting NAFLD-induced vascular changes, warrant further investigation of the IVIM3-NET in NAFLD patients.

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