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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