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Abstract #3469

DL-MOTIFDeep Learning Based Motion Transformation Integrated Forward-Fourier Reconstruction for Free-Breathing Liver DCE-MRI

Sihao Chen1, Weijie Gan1, Cihat Eldeniz1, Ulugbek S. Kamilov1, Tyler J. Fraum1, and Hongyu An1
1Washington University in St. Louis, Saint Louis, MO, United States


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 deep learning based motion transformation integrated forward-Fourier (DL-MOTIF) reconstruction using motion fields derived from a deep learning Phase2Phase (P2P) network and deep learning priors from a residual network on severely undersampled DCE. This approach reconstructs sharp motion-free DCE images with artifacts removal by incorporating deep learning motion fields for motion integration and deep learning priors for regularization.

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