MR Image Synthesis Using A Deep Learning Based Data-Driven Approach

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.