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Abstract #3175

Synthetic-to-real domain adaptation with deep learning for fitting IVIM-DWI parameters

Haoyuan Huang1, Baoer Liu2, Yikai Xu2, and Wu Zhou1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China


The intravoxel incoherent motion (IVIM) model of DWI with IVIM parameters has been widely used in characterization. However, the optimal method to obtain the IVIM parameters is still being explored. In this work, we propose a synthetic-to-real domain adaptation method for fitting the IVIM parameters. Specifically, we use synthesized data to train the network to learn the accurate mapping of the b-value images to the parameter map, and design a discriminator to help the network gradually adapt the learned mapping to the real data. Experimental results demonstrate that the proposed method outperforms previously reported methods for fitting IVIM parameters.

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