In this study, we demonstrate MR image synthesis using deep learning networks to generate six image contrasts (T1- and T2-weighted, T1 and T2 FLAIR, STIR, and PD) from a single multiple-dynamic multiple-echo (MDME) sequence. A convolutional encoder-decoder (CED) network was used to map axial slices of the MDME acquisition to the six different image contrasts. The synthesized images provide highly similar contrast and quality in comparison to the real acquired images for a variety of brain and non-brain tissues and demonstrate the robustness and potential of the data-driven deep learning approach.
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