Keywords: Machine Learning/Artificial Intelligence, Quantitative Imaging, Intravoxel incoherent motionRecently, various methods have been proposed to quantify intravoxel incoherent motion parameters. Many studies have shown that quantification methods using deep learning can accurately estimate IVIM parameters. Unsupervised learning is useful when quantifying IVIM parameters for in-vivo data because it does not require label data. However, in some cases, loss function does not converge as iteration increases. Constraint functions can be used to solve these problems by limiting the range of estimated outputs. In this study, we investigated the effects of constraint function to limit the range of estimated output.
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