Abstract #3512

Improving Synthetic MRI from Estimated Quantitative Maps with Deep Learning

Sidharth Kumar¹ and Jonathan I Tamir^1,2,3

¹Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States, ²Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States, ³Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, United States

Synopsis

Synthetic MRI has emerged as a tool for retrospectively generating contrast weightings from tissue parameter maps, but the generated contrasts can show mismatch due to unmodeled effects. We looked into the feasibility of refining arbitrary synthetic MRI contrasts using conditional GANs. To achieve this objective we trained a GAN on different experimentally obtained inversion recovery contrast images. As a proof of principle, the RefineNet is able to correct the contrast while losing out on finer structural details due to limited training data.

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