Dynamic contrast-enhanced MRI (DCE-MRI) of the liver offers structural and functional information for assessing the contrast uptake visually. However, respiratory motion and the requirement of high temporal resolution make it difficult to generate high-quality DCE-MRI. In this study, we proposed a novel forward-Fourier motion-corrected reconstruction utilizing deep learning based 3D motion information on severely undersampled DCE-MRI. With no need to use view-sharing or DCE contrast smoothness constraint, this approach avoids enhancement spillover from adjacent DCE contrasts and reconstructs high-quality motion-free DCE images with reduced artifacts and enhanced sharpness.
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