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Abstract #3433

Physically Motivated Deep-Neural Networks of the Intravoxel Incoherent Motion Signal Decay Model for Quantitative Diffusion-Weighted MRI

Shira Nemirovsky-Rotman1, Elad Rotman1, Onur Afacan2, Sila Kurugol2, Simon Warfield2, and Moti Freiman1
1Biomedical Engineering, Technion, Haifa, Israel, 2Boston's Children's Hospital, Harvard Medical School, Boston, MA, United States

Quantitative Diffusion-Weighted MRI with the Intra-Voxel Incoherent Motion (IVIM) model shows potential to produce quantitative biomarkers for multiple clinical applications. Recently, deep-learning (DL) models were proposed for the estimation of the IVIM model parameters from DW-MRI data. While the DL models produce more accurate parameter estimates compared to classical methods, their capability to generalize the IVIM model to different acquisition protocols is very limited. For that end, we introduce a physically motivated DL model, by incorporating the acquisition protocol into the network architecture. Our approach provides a DL-based method for IVIM parameter estimates that is agnostic to the acquisition protocol.

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