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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