We proposed a unified deep learning framework for predicting the motion deformation between different phases of 4D-MRI with simultaneous image quality enhancement. The network combines a coarse-to-fine unsupervised registration model to estimate the deformation vector fields (DVFs) in different image scales and a GAN-based enhancement network to restore anatomic features. Particularly, a prior knowledge of 4D-MRI is incorporated into the unified model, guiding an accurate DVF prediction and maintaining image topology. Both qualitative and quantitative results showed that the predicted DVFs and resultant 4D-MRI images achieved improved performance compared with the traditional method without modifications.
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