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Abstract #2531

SUPER-IVIM-DC,  A supervised deep-learning with data consistency approach for IVIM model parameter estimation from Diffusion-Weighted MRI data

Elad Rotman1, Onur Afacan2, Sila Kurugol2, Simon K Warfield2, and Moti Freiman1
1Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel, 2Computational Radiology lab, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States


Recently, unsupervised deep-learning showed improved performance in estimating the “Intra-Voxel incoherent motion” (IVIM) signal decay model parameters from Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data compared to classical methods. However, such deep-learning models do not generalize well on acquisitions with a high signal to noise ratio (SNR). In this work, we introduce SUPER-IVIM-DC, a supervised deep learning network coupled with a data consistency term to improve the capacity of deep-learning-based models to generalize the IVIM signal decay model. We demonstrated an improvement in model generalization, accuracy, and homogeneity using simulation, phantom, and in-vivo experiments.

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