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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords