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Abstract #3159

A unified deep learning method for 4D-MRI motion deformation estimation and image enhancement

Shaohua Zhi1, Haonan Xiao1, Yinghui Wang1, Wen Li1, Tian Li1, and Jing Cai1
1Department of Health Technology and Informatics, Hong Kong, China


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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