We implemented an improved unsupervised physics-informed deep neural network approach for intravoxel-incoherent motion modeling to DWI data by exploring several hyperparameters. Whereas the original IVIM-NETorig showed high dependency between the predicted IVIM parameters, our optimized approach resolved this high dependency, produced better accuracy and was more consistent. In simulations, IVIM-NEToptim outperformed least-squares and Bayesian fitting approaches. In patients with pancreatic ductal adenocarcinoma, IVIM-NEToptim produced substantially less noisy parameter maps and lower intersession within-subject standard deviations than the alternatives. IVIM-NEToptim also detected the most individual patients with significant parameter changes in the group of patients who received chemoradiotherapy.
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