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Abstract #0399

Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling

Misha P. T. Kaandorp1,2,3, Sebastiano Barbieri4, Remy Klaassen5, Hanneke W.M. van Laarhoven5, Hans Crezee6, Peter T. While2,3, Aart J. Nederveen1, and Oliver J. Gurney-Champion1
1Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands, 2Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway, 3Department of Circulation and Medical Imaging, NTNU: Norwegian University of Science and Technology, Trondheim, Norway, 4Centre for Big Data Research in Health, UNSW, Sydney, Australia, 5Department of Medical Oncology, Amsterdam UMC, Amsterdam, Netherlands, 6Department of Radiation Oncology, Amsterdam UMC, Amsterdam, Netherlands

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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